Add mDNS support

Not that important, but maybe a nice feature for the future: mDNS repeater like avahi struggle with the same problem that multicast is not supported on point-to-point connections like openvpn tun devices. Just using the mDNS port 5353 with udp-proxy-2020 doesn't work out of the box in a first test

Multicast seems supported on FreeBSD w/ OpenVPN:
ovpns2: flags=8051<UP,POINTOPOINT,RUNNING,MULTICAST> metric 0 mtu 1500
	options=80000<LINKSTATE>
	inet6 fe80::2e0:67ff:fe1f:4978%ovpns2 prefixlen 64 scopeid 0xf
	inet <IP_ADDRESS> --> <IP_ADDRESS> netmask 0xffffff00
	nd6 options=21<PERFORMNUD,AUTO_LINKLOCAL>
	groups: tun openvpn
	Opened by PID 79303
ovpnc1: flags=8051<UP,POINTOPOINT,RUNNING,MULTICAST> metric 0 mtu 1500
	options=80000<LINKSTATE>
	inet6 fe80::2e0:67ff:fe1f:4978%ovpnc1 prefixlen 64 scopeid 0x10
	inet <IP_ADDRESS> --> <IP_ADDRESS> netmask 0xffffffff
	nd6 options=21<PERFORMNUD,AUTO_LINKLOCAL>
	groups: tun openvpn
	Opened by PID 28427

Can you show the output of ifconfig on your UDM?

I will give you the infos tomorrow, meanwhile some hints for mDNS, if you want to know some details:
https://superuser.com/questions/949140/repeating-mdns-bonjour-requests-from-eth0-through-a-tunnel-tun0
https://community.ui.com/questions/mdns-repeater-with-OpenVPN-stun-v1-8-0/f0c0fc77-adb2-4881-a165-20abc10482f0

You're right, according to ifconfig the tun device should support multicast but obviously it doesn't work for mDNS

@tiehfood : I'm going to close this ticket as won't fix unless you or someone can actually tell me what I need to do.  Either why  udp-proxy-2020 doesn't work as-is or what it would need to do differently.  I suspect the answer is "decode the mDNS packets and modify them before forwarding", but the standard solution for this seems to be dns-sd instead?
Anyways, need more info.

@tiehfood : I'm going to close this ticket as won't fix unless you or someone can actually tell me what I need to do.  Either why  udp-proxy-2020 doesn't work as-is or what it would need to do differently.  I suspect the answer is "decode the mDNS packets and modify them before forwarding", but the standard solution for this seems to be dns-sd instead?
Anyways, need more info.

You could close it, if you don't have enough time to investigate the problem further. I have no deeper knowledge of mDNS, all I could tell is, that it doesn't work over the OpenVPN tun device with dns-sd, avahi or any other tool I found.
It would be a 'nice to have' feature (as I think your tool is the right way, to get it working) but it's not mandatory.
Great work so far for proxy Roon services on UDM!

You could close it, if you don't have enough time to investigate the problem further. I have no deeper knowledge of mDNS, all I could tell is, that it doesn't work over the OpenVPN tun device with dns-sd, avahi or any other tool I found.
It would be a 'nice to have' feature (as I think your tool is the right way, to get it working) but it's not mandatory.
Great work so far for proxy Roon services on UDM!


Add HTML  tag to core components

What is the expected behaviour?
Add the HTML <col> tag, as specified here: https://www.w3schools.com/tags/tag_col.asp

Please specify where to add.

Hey @rajkharvar, all our static components live in app/concepts/matestack/ui/core/. Feel free to take a look how similar components are defined, and also make sure to take a look at docs/components/ and spec/usage/components since we only allow tested and documented additions to our codebase :wink:
For help, don't hesitate to ping us in our Gitter channel

Okay, let me try.

@rajkharvar do you need any input or help with this issue? :wink:


Spread breaks nullable optional key check

This is wrong but doesn't throw an error:
type Foo = {
  info: string
}

function bar (old: Foo, stuff: {data?: any}): Foo {
  return {
    ...old,
    info: stuff.data && stuff.data.info
  }
}

If you don't do the spread it gives you the correct error (incompatible types)
function bar (old: Foo, stuff: {data?: any}): Foo {
  return {
    info: stuff.data && stuff.data.info
  }
}

A workaround is to make the value a maybe type, so this gives you a correct error too:
function bar (old: Foo, stuff: {data?: ?any}): Foo {
  return {
    ...old,
    info: stuff.data && stuff.data.info
  }
}

This will be fixed in an upcoming version of Flow 👍


Fetch User Blogs

Function getUserBlogs (Fetch blogs from specified User on specified Community)

Needs further fixing. See Commit. Some of the old changes will be reverted if we merge it now.


[Improvement] CRC Start console output should inform about developer account

General information

OS: macOS (but I suppose this output is on all OSs)
Hypervisor: VirtualBox (but I suppose this output is on all hypervisors)
Did you run crc setup before starting it (Yes/No)? yes

CRC version
version: 1.0.0-beta.3-4.1.11+e97bf65

Steps to reproduce

crc setup
crc start (+ needed parameters)
info about connection to openshift cluster is shown:

...
INFO Starting OpenShift cluster ... [waiting 3m]
INFO To access the cluster using 'oc', run 'eval $(crc oc-env) && oc login -u kubeadmin -p 78UVa-zNj5W-YB62Z-ggxGZ https://api.crc.testing:6443'
INFO Access the OpenShift web-console here: https://console-openshift-console.apps-crc.testing
INFO Login to the console with user: kubeadmin, password: 78UVa-zNj5W-YB62Z-ggxGZ

Expected
In info about connecting user to openshift cluster could be also information about developer account.
Actual
There is no info about developer account, only about kubeadmin.

This would make sense. Also, the developer account is used e.g. in this introduction to CRC.

On the master it is already fixed, so when we release beta-2 then it should be auto fixed.


Add initial sun awnings support

WORK IN PROGRESS
This is merely a work in progress and hasn't been tested or finished.
For more info see: https://github.com/rstrouse/ESPSomfy-RTS/issues/54

I believe this needs to be added to the storage and persistence file for the configuration as well.   It would be great if we could narrow this down to a bitmask instead of boolean values.  You mentioned wind originally how does this flag manifest itself.  Is it always on or can the wind sensor be turned on and off.  Is that the mysterious ::Flag command that has shown up?

I believe this needs to be added to the storage and persistence file for the configuration as well. It would be great if we could narrow this down to a bitmask instead of boolean values.

I'm not familiar with the project's code, so I need some help with that, but I'll look into it.

You mentioned wind originally how does this flag manifest itself. Is it always on or can the wind sensor be turned on and off. Is that the mysterious ::Flag command that has shown up?

Wind is always on and can't be turned off, so there are 2 possible modes: "Wind" or "Sun+Wind".
Yes, I think those modes would be ::Flag or ::SunFlag.

Ok it looks like I answered my question by RTFM at the same time you posted.
http://www.floridaautomatedshade.com/v/vspfiles/assets/catalogs/Sun and Wind Control/Soliris RTS Instruction Guide.pdf
The wind sensor cannot be disabled but the sun sensor can be turned on or off - got it.  So if you link the Soliris as if it were a linked remote it will retain the position of the awning.  If we recognize the sunflag on/off then this command can be used to link it and it will capture any up/down commands issued by the channel.
Based on what I am reading, it looks like some commands are ignored for up to 30 seconds or is this for Demo mode only.   Can this really be true?

The manual mentions an intermediate position.  Is this the My position.  If it is then this explains the my command being sent.  But I still can't wrap my head around how this is in RTS protocol not RTW.

I'm not familiar with the project's code, so I need some help with that, but I'll look into it.

No worries I will add an options field to the output.  There is too much data to store this in any other way than LittleFS because the PHY eats up all the storage when the ESP is an Ethernet device.  If you do a backup you will see the file that is stored.  Once we figure out how all the options are stored and whether we need 1 or 2 bytes to represent it I will add it to the storage.


sync: let manager listen at only the used ip

Codecov Report

Merging #1256 (952243f) into main (41a1f74) will increase coverage by 0.01%.
The diff coverage is 50.00%.

@@            Coverage Diff             @@
##             main    #1256      +/-   ##
==========================================
+ Coverage   57.91%   57.92%   +0.01%
==========================================
  Files         127      127
  Lines       21523    21523
==========================================
+ Hits        12465    12468       +3
+ Misses       7530     7527       -3
  Partials     1528     1528

Impacted Files
Coverage Δ

pkg/sync/cluster.go
32.55% <50.00%> (ø)

pkg/meta/sql.go
67.71% <0.00%> (+0.15%)
:arrow_up:

Continue to review full report at Codecov.

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Use a different url for etcd helm chart

When I run helm dependency update I got the following output:
Shawns-MBP:alluxio shawnsun$ helm dependency update
Getting updates for unmanaged Helm repositories...
...Unable to get an update from the "oci://registry-1.docker.io/bitnamicharts" chart repository:
	tag explicitly required
Error: repository oci://registry-1.docker.io/bitnamicharts is an OCI registry: this feature has been marked as experimental and is not enabled by default. Please set HELM_EXPERIMENTAL_OCI=1 in your environment to use this feature

Using the new url has no such error.

@lucyge2022 PTAL. Thanks!


Take a decision on devmode for ESP32-H2

According to tests devmode on H2 is not properly working.
There is no wifi, so it crashes when devmode starts.

and presumably same for p4, I suggest a board check here https://github.com/atomvm/AtomVM/blob/c7e0943e64cf5f7662170fd2a971a6c3bd6ced37/libs/esp32devmode/src/esp32devmode.erl#L85 for those two boards.

Good point about the P4. I hope to work on Ethernet support soon, so P4 could still use devmode eventually.


Using national identifiers

I don't think that the principle employed here  https://nhsconnect.github.io/fhir-policy/national-services.html#FHIR-NAT-01 is being followed for (e.g. NHS Numbers).

I can see this was incorrectly applied in the Appointment example, if there are any other instances please let me know. If not I'll close this ticket.

Need to check if all the profiles should use demographics url or fhir.nhs.uk?  I am not sure.  There is inconsistency on the site still.  Keep this open so we can discuss please. I am adding in a number of other new issues to be addressed in a new NWR.


CREATE FUNCTION with PARAMETERS - bug in documentation or functionality?

What is the correct syntax for creating an SQL function with a named parameter?
The documentation at http://orientdb.com/docs/2.1/SQL-Create-Function.html is not very clear about how the named parameter should be used in the function body.
I have tried numerous variants of the following:
CREATE FUNCTION get "SELECT FROM Person WHERE name = ?" PARAMETERS [VAL] LANGUAGE SQL;
The function is successfully defined, but cannot be successfully used.
Consider for example this transcript:
OrientDB console v.2.1.5 (build 2.1.x@r; 2015-10-29 16:54:25+0000) www.orientdb.com
...
orientdb {db=diner}> SELECT count(*) FROM Person WHERE name = "Bill"

----+------+-----
#   |@CLASS|count
----+------+-----
0   |null  |1
----+------+-----

1 item(s) found. Query executed in 0.002 sec(s).

orientdb {db=diner}> CREATE FUNCTION get "SELECT FROM Person WHERE name = ?" PARAMETERS [VAL] LANGUAGE SQL;

Function created successfully with id=#6:34

orientdb {db=diner}> select get("Bill")

----+------+----
#   |@CLASS|get
----+------+----
0   |null  |[0]
----+------+----

Note: For JAVASCRIPT functions, the situation is different, because one can (for example) write:
CREATE FUNCTION get 'return orient.getDatabase().query("select count(*) from " + val)[0]' PARAMETERS [val] LANGUAGE JAVASCRIPT

Thanks!

Maybe another reason to believe functions aren't supposed to be available using the console? :wink: :smiley:
Scott

@smolinari - Fortunately, your suggestion that the documentation on OrientDB-SQL is not to be taken seriously is at least in this case (i.e. in the case of http://orientdb.com/docs/2.1/SQL-Create-Function.html) simply refuted by the output of the console "help" command, which includes the line:
* create function <command-text>                                                       Create a stored function

Ok. That would mean the docs are missing and the function is broken.
Scott

By trial and error, I found that the key is the humble colon:
CREATE FUNCTION get "SELECT FROM V WHERE name = :VAL" PARAMETERS [VAL] LANGUAGE SQL IDEMPOTENT TRUE;

So this is a documentation issue/bug.
Unfortunately, the result of calling get(_) is not quite the same as the result obtained calling SELECT directly.  Documentation about that, I believe, is also needed.

Hello, Has this issue been resolved? Could someone kindly help with syntax/sample for writing a user-defined function in OrientDB using SQL language? Many thanks in advance.

Also, kindly provide the syntax to drop/delete a user-defined function from console.


Thoughts on supporting position and velocity PID control with Gazebo API

Hello! I saw some discussion about position and velocity PID control being removed from gazebo_ros2_control. I’m assuming this was to consolidate control logic in ros2_controllers effort_controllers/joint_position_controller and effort_controllers/joint_velocity_control?
The issue I’m running into is that using position or velocity interface with gazebo_ros2_control results in kinematic teleportation rather than simulated physics to move the joints. My guess is that the recommended solution is to use the effort controllers mentioned above (once they are ported to ros2_controllers).
However, I’m curious to hear what people think about adding support in gazebo_ros2_control to push the control logic to the simulation by using Gazebo’s JointController class in the GazeboSystem::write method? The advantage to this is that from an application level, it allows a direct swap between simulation and physical hardware by simply loading a different hardware interface plugin.
In my experience it is common to have motor controllers with position and velocity command interfaces so the ros2_controllers I typically use are joint_trajectory_controller (with position and/or velocity command interface), position_controllers/joint_group_position_controller, and velocity_controllers/joint_group_velocity_controller. By pushing the control logic to Gazebo, it would more directly mirror the physical hardware where joint-level control is handled by the motor controllers instead of at the robot application level. Then I could keep using the same ros2 controllers between simulation and hardware instead of having a ros2 control effort controller just for simulation.
Thoughts?

It took me a while to realize what you describe as "kinematic teleportation", see https://github.com/ros-controls/gazebo_ros2_control/pull/172 :smile:
What you propose makes sense to me and is the way that velocity interface is implemented with, e.g., Isaac Sim.
Also, the position interface of gz_ros2_control has a proportional gain using velocity interface then, but I'm not sure if there happens still the "kinematic teleportation" from the velocity command.
I had the same problem as you describe (velocity-controlled real hardware). I solved this by creating a fork of gazebo_ros2_control::GazeboSystem implementing a PID controller from the control_toolbox in combination with the simulation of actuator dynamics (hydraulics in my case).
How did you solve it in the end?


FIX: radomizing if starting player doesn't randomize at all

It doesn't look PERFECTLY random, but the randomization is working


Add event recorder into PipelineRun reconciler

What this PR dose
Add event recorder into PipelineRun reconciler
Why we need it
Please see https://github.com/kubesphere/ks-devops/pull/210#discussion_r699428922
Special notes

/kind feature

When the parameterized PipelineRun has no parameters spec, the warning event appears as follow:

If we want get more details about the error,  https://github.com/jenkins-zh/jenkins-client need to be enhanced.

/label tide/merge-method-rebase

Hi @LinuxSuRen , can this PR get ready for merging?


STAC Catalog vs. Collection

At present, we have 2 types of STAC Metadata within FAIRiCUBE:

Dataset Metadata
A/P Resource Metadata

Based on discussions with @sMorrone, we're seeing 2 additional metadata types emerging, that we'd like to utilize STAC for:

Knowledge Base Concepts
Insights on external datasets (datasets UC partners have investigated but then not used due to deficits identified. As this information could be valuable to other UC requiring similar data, we're proposing)

Based on these different types, I'm wondering of the STAC Collection (or Catalog) concept wouldn't be valuable to group these 4 different metadata types.

Absolutely, it seems we'll have 4 STAC catalogs at the first level merged together by a single FiC STAC catalog to be searchable in the FiC catalog client via STAC API in STAC Browser. I don't think STAC collection would be the right concept on this level as we'd have to add common metadata.
FYI, for dataset metadata below the initial STAC catalog mostly STAC collections are used. I suppose that will be the same for the other three concepts.

Do I understand correctly that collections are only used when a dataset is only being expanded along one dimension (or similar concept. I know that a series of satellite images varies across 2 dimensions), catalogs for more general grouping?

Yes, that is also my understanding, Catalogs are for general grouping and Collections for Items or further Collections that share some commonality.

there seem to be general agreement, can the ticket be closed or are there any follow up action?

Closing with the agreement of the following catalogs for FAIRiCUBE:

Dataset Metadata
A/P ML Resource Metadata
A/P non-ML Resource Metadata

Potentially we will add additional catalogs for:

Knowledge Base Concepts
Insights on external datasets (datasets UC partners have investigated but then not used due to deficits identified. As this information could be valuable to other UC requiring similar data, we're proposing)


Search doesn't consider :text qualifier for string parameters

String search parameters including a :text qualifier should perform an advanced search (https://www.hl7.org/fhir/search.html#content) as spec indicates. Instead, the qualifier is ignored.
To Reproduce
Steps to reproduce the behavior:

Create Observation including a string value of "Cloudy, yellow"
Search using the following query:  /Observation?value-string:text=yellow
Observation is not found

Expected behaviour
Created Observation should be found
Environment:

HAPI FHIR Version v6.1.2

Fixed by: https://github.com/hapifhir/hapi-fhir/pull/4047


enable gomnd linter

I hereby agree to the terms of the CLA available at: https://yandex.ru/legal/cla/?lang=en

Pull request type

Please check the type of change your PR introduces:

[ ] Bugfix
[ ] Feature
[x] Code style update (formatting, renaming)
[x] Refactoring (no functional changes, no api changes)
[x] Build related changes
[ ] Documentation content changes
[ ] Other (please describe):

What is the current behavior?

Issue Number: #943
What is the new behavior?

Other information

Codecov Report
Attention: 14 lines in your changes are missing coverage. Please review.

Comparison is base (7d5e6a0) 66.20% compared to head (2849611) 38.63%.

Files
Patch %
Lines

internal/table/scanner/scanner.go
0.00%
6 Missing :warning:

internal/value/time.go
40.00%
0 Missing and 3 partials :warning:

internal/conn/pool.go
0.00%
1 Missing :warning:

internal/credentials/static.go
0.00%
1 Missing :warning:

internal/meta/context.go
0.00%
1 Missing :warning:

internal/value/value.go
50.00%
1 Missing :warning:

retry/retry.go
0.00%
1 Missing :warning:

Additional details and impacted files
@@             Coverage Diff             @@
##           master    #1048       +/-   ##
===========================================
- Coverage   66.20%   38.63%   -27.57%
===========================================
  Files         255      209       -46
  Lines       26345    22092     -4253
===========================================
- Hits        17441     8535     -8906
- Misses       8057    13199     +5142
+ Partials      847      358      -489

Flag
Coverage Δ

?

go-1.21.x
38.63% <54.83%> (-29.49%)
:arrow_down:

go-1.22.x
?

integration
?

macOS
38.63% <54.83%> (+1.42%)
:arrow_up:

ubuntu
?

unit
38.63% <54.83%> (+1.34%)
:arrow_up:

windows
?

ydb-22.5
?

ydb-23.2
?

ydb-23.3
?

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Unable to run the script

Problem:
When I try, to run the script, I get the following error:
Looking for interesting users for Radu_Raicea...
Retrieving user ID...
Traceback (most recent call last):
  File "finder.py", line 178, in <module>
    interesting_users = get_interesting_users('Radu_Raicea', 10)
  File "finder.py", line 165, in get_interesting_users
    user_id = get_user_id(username)
  File "finder.py", line 23, in get_user_id
    response_dict = clean_json_response(response)
  File "finder.py", line 13, in clean_json_response
    return json.loads(response.text.split('])}while(1);</x>')[1])
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/__init__.py", line 338, in loads
    return _default_decoder.decode(s)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/decoder.py", line 366, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/decoder.py", line 382, in raw_decode
    obj, end = self.scan_once(s, idx)
ValueError: Unterminated string starting at: line 1 column 17383 (char 17382)

Found the bug, it's quite funny!
The clean_json_response splits the response at ])}while(1);</x> in order to remove it. I recently explained the meaning of ])}while(1);</x> in one of my responses. When loading my profile, it loads a part of the response where I mention the string ])}while(1);</x>. clean_json_response splits the response there too.
In order to fix this, clean_json_response must only remove the first iteration of ])}while(1);</x>.

Fixed by #4


backport yara patch for macOS

This PR is simple backports https://github.com/VirusTotal/yara/pull/1540 to existed way apply patches to yara which is fixed #990

Lets run TC tests.


torchtune generate function error when model used Int4WeightOnlyQATQuantizer

today, i try to use Int4WeightOnlyQATQuantizer to quantize llama3-8b
when i use model generate function, i get below error:
Running InferenceRecipe with resolved config:

chat_format: null
checkpointer:
  _component_: torchtune.training.FullModelTorchTuneCheckpointer
  checkpoint_dir: /QAT/output/llama3-8B/
  checkpoint_files:
  - meta_model_0-4w-qat-module-swap.pt
  model_type: LLAMA3
  output_dir: /QAT/output/llama3-8B/
device: cuda
dtype: bf16
enable_kv_cache: true
instruct_template: null
max_new_tokens: 300
model:
  _component_: torchtune.models.llama3.llama3_8b
prompt: Tell me a joke?
quantizer:
  _component_: torchtune.training.quantization.Int4WeightOnlyQuantizer
  groupsize: 256
seed: 42
temperature: 0.6
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  max_seq_len: null
  path: /QAT/Meta-Llama-3-8B/original/tokenizer.model
top_k: 1

Setting manual seed to local seed 42. Local seed is seed + rank = 42 + 0
Traceback (most recent call last):
  File "/usr/local/bin/tune", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/torchtune/_cli/tune.py", line 49, in main
    parser.run(args)
  File "/usr/local/lib/python3.10/dist-packages/torchtune/_cli/tune.py", line 43, in run
    args.func(args)
  File "/usr/local/lib/python3.10/dist-packages/torchtune/_cli/run.py", line 208, in _run_cmd
    self._run_single_device(args, is_builtin=is_builtin)
  File "/usr/local/lib/python3.10/dist-packages/torchtune/_cli/run.py", line 102, in _run_single_device
    runpy.run_path(str(args.recipe), run_name="__main__")
  File "/usr/lib/python3.10/runpy.py", line 289, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/usr/lib/python3.10/runpy.py", line 96, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.10/dist-packages/recipes/generate.py", line 229, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/torchtune/config/_parse.py", line 99, in wrapper
    sys.exit(recipe_main(conf))
  File "/usr/local/lib/python3.10/dist-packages/recipes/generate.py", line 224, in main
    recipe.setup(cfg=cfg)
  File "/usr/local/lib/python3.10/dist-packages/recipes/generate.py", line 70, in setup
    self._model = self._setup_model(
  File "/usr/local/lib/python3.10/dist-packages/recipes/generate.py", line 89, in _setup_model
    model.load_state_dict(model_state_dict, assign=True)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2584, in load_state_dict
    raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for TransformerDecoder:
        Unexpected key(s) in state_dict: "layers.0.attn.q_proj.scales_and_zeros", "layers.0.attn.k_proj.scales_and_zeros", "layers.0.attn.v_proj.scales_and_zeros", "layers.0.attn.output_proj.scales_and_zeros", "layers.0.mlp.w1.scales_and_zeros", "layers.0.mlp.w2.scales_and_zeros", "layers.0.mlp.w3.scales_and_zeros",
"layers.1.attn.q_proj.scales_and_zeros", "layers.1.attn.k_proj.scales_and_zeros", "layers.1.attn.v_proj.scales_and_zeros", "layers.1.attn.output_proj.scales_and_zeros", "layers.1.mlp.w1.scales_and_zeros", "layers.1.mlp.w2.scales_and_zeros", "layers.1.mlp.w3.scales_and_zeros", "layers.2.attn.q_proj.scales_and_zeros",
"layers.2.attn.k_proj.scales_and_zeros", "layers.2.attn.v_proj.scales_and_zeros", "layers.2.attn.output_proj.scales_and_zeros", "layers.2.mlp.w1.scales_and_zeros", "layers.2.mlp.w2.scales_and_zeros", "layers.2.mlp.w3.scales_and_zeros", "layers.3.attn.q_proj.scales_and_zeros", "layers.3.attn.k_proj.scales_and_zeros",
"layers.3.attn.v_proj.scales_and_zeros", "layers.3.attn.output_proj.scales_and_zeros", "layers.3.mlp.w1.scales_and_zeros", "layers.3.mlp.w2.scales_and_zeros", "layers.3.mlp.w3.scales_and_zeros", "layers.4.attn.q_proj.scales_and_zeros", "layers.4.attn.k_proj.scales_and_zeros", "layers.4.attn.v_proj.scales_and_zeros",
"layers.4.attn.output_proj.scales_and_zeros", "layers.4.mlp.w1.scales_and_zeros", "layers.4.mlp.w2.scales_and_zeros", "layers.4.mlp.w3.scales_and_zeros", "layers.5.attn.q_proj.scales_and_zeros", "layers.5.attn.k_proj.scales_and_zeros", "layers.5.attn.v_proj.scales_and_zeros", "layers.5.attn.output_proj.scales_and_zer
os", "layers.5.mlp.w1.scales_and_zeros", "layers.5.mlp.w2.scales_and_zeros", "layers.5.mlp.w3.scales_and_zeros", "layers.6.attn.q_proj.scales_and_zeros", "layers.6.attn.k_proj.scales_and_zeros", "layers.6.attn.v_proj.scales_and_zeros", "layers.6.attn.output_proj.scales_and_zeros", "layers.6.mlp.w1.scales_and_zeros",
"layers.6.mlp.w2.scales_and_zeros", "layers.6.mlp.w3.scales_and_zeros", "layers.7.attn.q_proj.scales_and_zeros", "layers.7.attn.k_proj.scales_and_zeros", "layers.7.attn.v_proj.scales_and_zeros", "layers.7.attn.output_proj.scales_and_zeros", "layers.7.mlp.w1.scales_and_zeros", "layers.7.mlp.w2.scales_and_zeros", "laye
rs.7.mlp.w3.scales_and_zeros", "layers.8.attn.q_proj.scales_and_zeros", "layers.8.attn.k_proj.scales_and_zeros", "layers.8.attn.v_proj.scales_and_zeros", "layers.8.attn.output_proj.scales_and_zeros", "layers.8.mlp.w1.scales_and_zeros", "layers.8.mlp.w2.scales_and_zeros", "layers.8.mlp.w3.scales_and_zeros", "layers.9.
attn.q_proj.scales_and_zeros", "layers.9.attn.k_proj.scales_and_zeros", "layers.9.attn.v_proj.scales_and_zeros", "layers.9.attn.output_proj.scales_and_zeros", "layers.9.mlp.w1.scales_and_zeros", "layers.9.mlp.w2.scales_and_zeros", "layers.9.mlp.w3.scales_and_zeros", "layers.10.attn.q_proj.scales_and_zeros", "layers.1
0.attn.k_proj.scales_and_zeros", "layers.10.attn.v_proj.scales_and_zeros", "layers.10.attn.output_proj.scales_and_zeros", "layers.10.mlp.w1.scales_and_zeros", "layers.10.mlp.w2.scales_and_zeros", "layers.10.mlp.w3.scales_and_zeros", "layers.11.attn.q_proj.scales_and_zeros", "layers.11.attn.k_proj.scales_and_zeros", "
layers.11.attn.v_proj.scales_and_zeros", "layers.11.attn.output_proj.scales_and_zeros", "layers.11.mlp.w1.scales_and_zeros", "layers.11.mlp.w2.scales_and_zeros", "layers.11.mlp.w3.scales_and_zeros", "layers.12.attn.q_proj.scales_and_zeros", "layers.12.attn.k_proj.scales_and_zeros", "layers.12.attn.v_proj.scales_and_z
eros", "layers.12.attn.output_proj.scales_and_zeros", "layers.12.mlp.w1.scales_and_zeros", "layers.12.mlp.w2.scales_and_zeros", "layers.12.mlp.w3.scales_and_zeros", "layers.13.attn.q_proj.scales_and_zeros", "layers.13.attn.k_proj.scales_and_zeros", "layers.13.attn.v_proj.scales_and_zeros", "layers.13.attn.output_proj
.scales_and_zeros", "layers.13.mlp.w1.scales_and_zeros", "layers.13.mlp.w2.scales_and_zeros", "layers.13.mlp.w3.scales_and_zeros", "layers.14.attn.q_proj.scales_and_zeros", "layers.14.attn.k_proj.scales_and_zeros", "layers.14.attn.v_proj.scales_and_zeros", "layers.14.attn.output_proj.scales_and_zeros", "layers.14.mlp
.w1.scales_and_zeros", "layers.14.mlp.w2.scales_and_zeros", "layers.14.mlp.w3.scales_and_zeros", "layers.15.attn.q_proj.scales_and_zeros", "layers.15.attn.k_proj.scales_and_zeros", "layers.15.attn.v_proj.scales_and_zeros", "layers.15.attn.output_proj.scales_and_zeros", "layers.15.mlp.w1.scales_and_zeros", "layers.15.
mlp.w2.scales_and_zeros", "layers.15.mlp.w3.scales_and_zeros", "layers.16.attn.q_proj.scales_and_zeros", "layers.16.attn.k_proj.scales_and_zeros", "layers.16.attn.v_proj.scales_and_zeros", "layers.16.attn.output_proj.scales_and_zeros", "layers.16.mlp.w1.scales_and_zeros", "layers.16.mlp.w2.scales_and_zeros", "layers.
16.mlp.w3.scales_and_zeros", "layers.17.attn.q_proj.scales_and_zeros", "layers.17.attn.k_proj.scales_and_zeros", "layers.17.attn.v_proj.scales_and_zeros", "layers.17.attn.output_proj.scales_and_zeros", "layers.17.mlp.w1.scales_and_zeros", "layers.17.mlp.w2.scales_and_zeros", "layers.17.mlp.w3.scales_and_zeros", "laye
rs.18.attn.q_proj.scales_and_zeros", "layers.18.attn.k_proj.scales_and_zeros", "layers.18.attn.v_proj.scales_and_zeros", "layers.18.attn.output_proj.scales_and_zeros", "layers.18.mlp.w1.scales_and_zeros", "layers.18.mlp.w2.scales_and_zeros", "layers.18.mlp.w3.scales_and_zeros", "layers.19.attn.q_proj.scales_and_zeros
", "layers.19.attn.k_proj.scales_and_zeros", "layers.19.attn.v_proj.scales_and_zeros", "layers.19.attn.output_proj.scales_and_zeros", "layers.19.mlp.w1.scales_and_zeros", "layers.19.mlp.w2.scales_and_zeros", "layers.19.mlp.w3.scales_and_zeros", "layers.20.attn.q_proj.scales_and_zeros", "layers.20.attn.k_proj.scales_a
nd_zeros", "layers.20.attn.v_proj.scales_and_zeros", "layers.20.attn.output_proj.scales_and_zeros", "layers.20.mlp.w1.scales_and_zeros", "layers.20.mlp.w2.scales_and_zeros", "layers.20.mlp.w3.scales_and_zeros", "layers.21.attn.q_proj.scales_and_zeros", "layers.21.attn.k_proj.scales_and_zeros", "layers.21.attn.v_proj.
scales_and_zeros", "layers.21.attn.output_proj.scales_and_zeros", "layers.21.mlp.w1.scales_and_zeros", "layers.21.mlp.w2.scales_and_zeros", "layers.21.mlp.w3.scales_and_zeros", "layers.22.attn.q_proj.scales_and_zeros", "layers.22.attn.k_proj.scales_and_zeros", "layers.22.attn.v_proj.scales_and_zeros", "layers.22.attn
.output_proj.scales_and_zeros", "layers.22.mlp.w1.scales_and_zeros", "layers.22.mlp.w2.scales_and_zeros", "layers.22.mlp.w3.scales_and_zeros", "layers.23.attn.q_proj.scales_and_zeros", "layers.23.attn.k_proj.scales_and_zeros", "layers.23.attn.v_proj.scales_and_zeros", "layers.23.attn.output_proj.scales_and_zeros", "l
ayers.23.mlp.w1.scales_and_zeros", "layers.23.mlp.w2.scales_and_zeros", "layers.23.mlp.w3.scales_and_zeros", "layers.24.attn.q_proj.scales_and_zeros", "layers.24.attn.k_proj.scales_and_zeros", "layers.24.attn.v_proj.scales_and_zeros", "layers.24.attn.output_proj.scales_and_zeros", "layers.24.mlp.w1.scales_and_zeros",
 "layers.24.mlp.w2.scales_and_zeros", "layers.24.mlp.w3.scales_and_zeros", "layers.25.attn.q_proj.scales_and_zeros", "layers.25.attn.k_proj.scales_and_zeros", "layers.25.attn.v_proj.scales_and_zeros", "layers.25.attn.output_proj.scales_and_zeros", "layers.25.mlp.w1.scales_and_zeros", "layers.25.mlp.w2.scales_and_zero
s", "layers.25.mlp.w3.scales_and_zeros", "layers.26.attn.q_proj.scales_and_zeros", "layers.26.attn.k_proj.scales_and_zeros", "layers.26.attn.v_proj.scales_and_zeros", "layers.26.attn.output_proj.scales_and_zeros", "layers.26.mlp.w1.scales_and_zeros", "layers.26.mlp.w2.scales_and_zeros", "layers.26.mlp.w3.scales_and_z
eros", "layers.27.attn.q_proj.scales_and_zeros", "layers.27.attn.k_proj.scales_and_zeros", "layers.27.attn.v_proj.scales_and_zeros", "layers.27.attn.output_proj.scales_and_zeros", "layers.27.mlp.w1.scales_and_zeros", "layers.27.mlp.w2.scales_and_zeros", "layers.27.mlp.w3.scales_and_zeros", "layers.28.attn.q_proj.scal
es_and_zeros", "layers.28.attn.k_proj.scales_and_zeros", "layers.28.attn.v_proj.scales_and_zeros", "layers.28.attn.output_proj.scales_and_zeros", "layers.28.mlp.w1.scales_and_zeros", "layers.28.mlp.w2.scales_and_zeros", "layers.28.mlp.w3.scales_and_zeros", "layers.29.attn.q_proj.scales_and_zeros", "layers.29.attn.k_p
roj.scales_and_zeros", "layers.29.attn.v_proj.scales_and_zeros", "layers.29.attn.output_proj.scales_and_zeros", "layers.29.mlp.w1.scales_and_zeros", "layers.29.mlp.w2.scales_and_zeros", "layers.29.mlp.w3.scales_and_zeros", "layers.30.attn.q_proj.scales_and_zeros", "layers.30.attn.k_proj.scales_and_zeros", "layers.30.
attn.v_proj.scales_and_zeros", "layers.30.attn.output_proj.scales_and_zeros", "layers.30.mlp.w1.scales_and_zeros", "layers.30.mlp.w2.scales_and_zeros", "layers.30.mlp.w3.scales_and_zeros", "layers.31.attn.q_proj.scales_and_zeros", "layers.31.attn.k_proj.scales_and_zeros", "layers.31.attn.v_proj.scales_and_zeros", "la
yers.31.attn.output_proj.scales_and_zeros", "layers.31.mlp.w1.scales_and_zeros", "layers.31.mlp.w2.scales_and_zeros", "layers.31.mlp.w3.scales_and_zeros", "output.scales_and_zeros".
        size mismatch for layers.0.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.0.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.0.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.0.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.0.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.0.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.0.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.1.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.1.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.1.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.1.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.1.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.1.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.1.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.2.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.2.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.2.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.2.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.2.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.2.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.2.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.3.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.3.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.3.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.3.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.3.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.3.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.3.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.4.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.4.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.4.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.4.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.4.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.4.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.4.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.5.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.5.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.5.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.5.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.5.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.5.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.5.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.6.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.6.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.6.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.6.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.6.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.6.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.6.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.7.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.7.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.7.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.7.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.7.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.7.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.7.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.8.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.8.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.8.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.8.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.8.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.8.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.8.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.9.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.9.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.9.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.9.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.9.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.9.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.9.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.10.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.10.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.10.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.10.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.10.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.10.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.10.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.11.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.11.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.11.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.11.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.11.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.11.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.11.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.12.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.12.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.12.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.12.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.12.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.12.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.12.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.13.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.13.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.13.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.13.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.13.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.13.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.13.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.14.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.14.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.14.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.14.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.14.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.14.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.14.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.15.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.15.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.15.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.15.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.15.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.15.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.15.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.16.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.16.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.16.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.16.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.16.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.16.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.16.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.17.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.17.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.17.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.17.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.17.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.17.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.17.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.18.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.18.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.18.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.18.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.18.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.18.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.18.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.19.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.19.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.19.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.19.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.19.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.19.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.19.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.20.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.20.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.20.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.20.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.20.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.20.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.20.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.21.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.21.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.21.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.21.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.21.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.21.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.21.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.22.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.22.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.22.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.22.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.22.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.22.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.22.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.23.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.23.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.23.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.23.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.23.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.23.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.23.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.24.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.24.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.24.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.24.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.24.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.24.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.24.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.25.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.25.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.25.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.25.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.25.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.25.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.25.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.26.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.26.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.26.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.26.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.26.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.26.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.26.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.27.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.27.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.27.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.27.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.27.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.27.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.27.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.28.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.28.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.28.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.28.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.28.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.28.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.28.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.29.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.29.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.29.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.29.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.29.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.29.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.29.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.30.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.30.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.30.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.30.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.30.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.30.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.30.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.31.attn.q_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.31.attn.k_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.31.attn.v_proj.weight: copying a param with shape torch.Size([128, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
        size mismatch for layers.31.attn.output_proj.weight: copying a param with shape torch.Size([512, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.31.mlp.w1.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for layers.31.mlp.w2.weight: copying a param with shape torch.Size([512, 112, 32, 4]) from checkpoint, the shape in current model is torch.Size([4096, 14336]).
        size mismatch for layers.31.mlp.w3.weight: copying a param with shape torch.Size([1792, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([14336, 4096]).
        size mismatch for output.weight: copying a param with shape torch.Size([16032, 32, 32, 4]) from checkpoint, the shape in current model is torch.Size([128256, 4096]).

package version:
torch                             2.6.0.dev20241009+cu121
torchao                           0.7.0.dev20241010+cu121
torchtune                         0.4.0.dev20241010+cpu
torchvision                       0.20.0.dev20241009+cu121

@elfisworking thanks for creating the issue. I intended to look at this today but unfortunately ran out of time before I could get to it, I am gonna tag as hi-pri to ensure someone takes a closer look asap


bug with resumableChunkNumber

Hi,
when uploading multiple files since the uploader window, resumableChunkNumber are mixed up between files, so we can have 1, 2 , 3 , 7 on first file then 1, 2, 4, 5 on second (the 3 first chunks of the second file take place of the 4th , 5th and 6th chunks), those numbers are an exemple ... but as you can see it's all mixed up !

Hi @cpaulet -- can you help me with an example url where I can see this happen?

Hi, unfortunately, i work in local, but if you try with this code :
[code]
@section('main')
Select files

</div>

@endsection
@section('scripts')

@endsection
@section('javascript')


The C# library is missing the new app portal interface that supports the feature flags

Feature Request
Motivation
See this code example
var svix = new SvixClient("AUTH_TOKEN", new SvixOptions("https://api.svix.com/"));
var dashboard = await svix.Authentication.AppPortalAccessAsync("app_Xzx8bQeOB1D1XEYmAJaRGoj0", new AppPortalAccessIn{
    featureFlags: new string[] {"beta-feature-a"}
});
// A URL that automatically logs user into the dashboard
Console.WriteLine(dashboard.Url)

The SvixClient class has a property IAuthentication Authentication .  However the IAuthentication interface does not have a method AppPortalAccessAsync.
It does have a method GetDashboardAccessAsync which works well, but does not allow to pass feature flags.
Proposal
See this slack thread

Whoops, missed this. Will get this added asap, sorry for the delay!


Only show applicants from shown admission

Current implementation of unlogged view is buggy as it fetches applicants from all admissions. Pretty sub-optimal, actually :-)

Closing because it will be further improved by another PR, which will contain this code.


fix:  g版本升级，supportsCSSTransform=true是默认设置

Checklist

[x] npm test passes
[x] benchmarks are included
[x] commit message follows commit guidelines
[x] documents are updated

Description of change
解决类型错误：Property 'supportsCSSTransform' does not exist on type 'CanvasConfig'
底层依赖g版本升级，supportsCSSTransform=true是默认设置，g2中不再需要设置supportsCSSTransform属性

Pull Request Test Coverage Report for Build<PHONE_NUMBER>0
Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.007%) to 86.697%

Totals

Change from base Build<PHONE_NUMBER>1:
-0.007%

Covered Lines:
10613

Relevant Lines:
11865

💛  - Coveralls


Hang when running NavMeshBuilder.BuildNavMeshData()

I am attempting to run BuildNavMesh() on meshes generated from AR polygon data. The application freezes when the code enters NavMeshBuilder.BuildNavMeshData(). The sources and bounds look reasonable before entering, I can only assume the mesh is degenerate in some way.
There's no easy way to get the mesh back without some unwieldy hackery, but is there any particular mesh formation that would cause the build to get stuck so I can investigate a little further and build a repro?

I can't think of any specific mesh configuration that would cause BuildNavMeshData() to freeze the application. Could you submit please a small project with which we could reproduce the issue?
The only similar problem I encountered was when the editor would freeze because the geometry mesh had large areas, completely flat, composed of a huge number of triangles, and the algorithm was struggling to produce the height mesh. But that is only the case when building a static NavMesh from the editor's Navigation window (or NavMeshBuilder.BuildNavMesh() ), not when using NavMeshBuilder.BuildNavMeshData() (which does not compute a height mesh).

I have some private Unity-owned code in there, can you email me at kalin at funktroniclabs.com, and I can send you the repro build.

(or give me a contact I can send it to!)

For reference, it seems this was caused by two NavMeshSurface components on a single object.


Publishing crash reports flaked, failed integration tests

https://dev.azure.com/monacotools/Monaco/_build/results?buildId=194862&view=logs&j=a8b516b9-5c25-5ad9-505a-b03d38bc1dad&t=726cfe94-b13b-50e3-ba83-4f78315cd046&l=9

The real error is ECONNRESET when downloading Electron, which can happen randomly. Will keep an eye open for repeats.


⚠ Diverse AirCon & Mechanical has degraded performance

In 9078089, Diverse AirCon & Mechanical (https://diverseaircon.com.au) experienced degraded performance:

HTTP code: 200
Response time: 9240 ms

Resolved: Diverse AirCon & Mechanical performance has improved in a122fc7.