Instructions to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("continuum-ai/mixtral-8x7b-instruct-compacted-conservative")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="continuum-ai/mixtral-8x7b-instruct-compacted-conservative",
	filename="mixtral-8x7b-compacted-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Use Docker

docker model run hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

LM Studio
Jan

vLLM

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "continuum-ai/mixtral-8x7b-instruct-compacted-conservative"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "continuum-ai/mixtral-8x7b-instruct-compacted-conservative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Ollama
How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with Ollama:
```
ollama run hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
```

Unsloth Studio

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for continuum-ai/mixtral-8x7b-instruct-compacted-conservative to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for continuum-ai/mixtral-8x7b-instruct-compacted-conservative to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for continuum-ai/mixtral-8x7b-instruct-compacted-conservative to start chatting

MLX LM

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "continuum-ai/mixtral-8x7b-instruct-compacted-conservative"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "continuum-ai/mixtral-8x7b-instruct-compacted-conservative"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "continuum-ai/mixtral-8x7b-instruct-compacted-conservative",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with Docker Model Runner:
```
docker model run hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M
```

Lemonade

How to use continuum-ai/mixtral-8x7b-instruct-compacted-conservative with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull continuum-ai/mixtral-8x7b-instruct-compacted-conservative:Q4_K_M

Run and chat with the model

lemonade run user.mixtral-8x7b-instruct-compacted-conservative-Q4_K_M

List all available models

lemonade list

mixtral-8x7b-instruct-compacted-conservative / README.md

EnricoFermi

fix: use hf.co (CORS-open) in verify URLs

8ec99b9 verified about 2 months ago

preview code

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	---
	tags:
	- 35b
	- android
	- apple-silicon
	- attested
	- chain-of-custody
	- consumer-gpu
	- cryptographically-verified
	- edge-inference
	- efficient
	- english
	- expert-pruning
	- forge-alloy
	- general
	- gguf
	- instruct
	- llama-cpp
	- lm-studio
	- local-inference
	- macbook
	- mixture-of-experts
	- mlx
	- mobile
	- moe
	- moe-compaction
	- multilingual
	- ollama
	- on-device
	- optimized
	- pruned
	- q4_k_m
	- quantized
	- raspberry-pi
	- reproducible
	- sparse-moe
	- text-generation
	- versatile
	- calibration-aware-pruning
	- mixtral
	base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
	pipeline_tag: text-generation
	license: apache-2.0
	---

	# 25% Experts Pruned, PPL 8.97 (base 8.14)

	Mixtral-8x7B-Instruct-v0.1 compacted via calibration-aware MoE expert pruning (§4.1.3.4) against the unmodified source.

	- Perplexity: 8.97 (base 8.14, Δ +10.2%)
	- Compression: 93.4 GB → 20.4 GB Q4_K_M (4.6×)
	- Throughput: 142 tok/s generation, 437 tok/s prompt on RTX 5090


	<p align="center">
	<a href="https://cambriantech.github.io/forge-alloy/verify/#hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative/resolve/main/mixtral-8x7b-instruct-compacted-conservative.alloy.json@b26fd7adf36b7c8c">
	<img src="alloy-qr.png" alt="Verify Chain of Custody" width="160"/>
	</a>
	</p>

	<p align="center">
	<a href="https://cambriantech.github.io/forge-alloy/verify/#hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative/resolve/main/mixtral-8x7b-instruct-compacted-conservative.alloy.json@b26fd7adf36b7c8c"><b>Every claim on this card is verified</b></a><br>
	<b>Trust: self-attested</b> · 1 benchmark · 1 device tested<br>
	<a href="https://github.com/CambrianTech/forge-alloy">ForgeAlloy</a> chain of custody · <a href="mixtral-8x7b-instruct-compacted-conservative.alloy.json">Download alloy</a> · Merkle-chained
	</p>

	---

	A 93 GB datacenter MoE compressed to run on a MacBook Air. Forged from `mistralai/Mixtral-8x7B-Instruct-v0.1` by removing the 2 least-activated experts per layer (8→6) via calibration-aware activation-frequency ranking on a held-out code corpus (300 examples, 148,945 tokens). Quantized to GGUF Q4_K_M for llama.cpp / Ollama / LM Studio. Apache-2.0. PPL 8.97 against the source's 8.14 (Δ +10.2%), evaluated via llama.cpp on wikitext-2-raw. Second row of the [cross-family anchor table](#cross-family-anchor-table). Cryptographic provenance via [ForgeAlloy](https://github.com/CambrianTech/forge-alloy).


	## Benchmarks

	\| Benchmark \| Score \| Base \| Δ \| Verified \|
	\|---\|---\|---\|---\|---\|
	\| wikitext-2-raw PPL \| 8.97 \| 8.14 \| +10.2% \| ✅ Result hash \|


	## What Changed (Base → Forged)

	\| \| Base \| Forged \| Delta \|
	\|---\|---\|---\|---\|
	\| Perplexity \| 8.14 \| 8.97 \| +10.2% \|
	\| Experts / layer \| 8 \| 6 \| −25% (2 removed per layer) \|
	\| Total params \| 46.7B \| ~35B \| −25% \|
	\| Active params \| 12.9B \| 12.9B \| Unchanged \|
	\| Size (fp16) \| 93.4 GB \| 70.9 GB \| −24% \|
	\| Size (Q4_K_M) \| — \| 20.4 GB \| 4.6× compression \|
	\| Pipeline \| \| expert-activation-profile → expert-prune → quant → eval \| 1 cycle \|


	## Runs On

	\| Device \| Format \| Size \| Speed \|
	\|--------\|--------\|------\|-------\|
	\| NVIDIA GeForce RTX 5090 \| Q4_K_M \| 20.4 GB \| 142 tok/s generation ✅ Verified \|
	\| MacBook Pro 32GB \| Q4_K_M \| 20.4 GB \| Expected \|
	\| MacBook Air 24GB \| Q4_K_M \| 20.4 GB \| Expected \|
	\| RTX 3060 12GB+ \| Q4_K_M \| 20.4 GB \| Expected (partial offload) \|
	\| RTX 4090 24GB \| Q4_K_M \| 20.4 GB \| Expected \|
	\| RTX 4090 24GB \| fp16 \| 70.9 GB \| Expected (with offload) \|


	## Quick Start

	```bash
	# llama.cpp (any platform)
	./llama-cli -m mixtral-8x7b-compacted-Q4_K_M.gguf \
	-p "Write a Python function that finds the longest palindromic substring." \
	-n 512 -ngl 99

	# Ollama
	ollama run continuum-ai/mixtral-8x7b-instruct-compacted-conservative
	```

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"continuum-ai/mixtral-8x7b-instruct-compacted-conservative",
	torch_dtype="auto", device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained(
	"continuum-ai/mixtral-8x7b-instruct-compacted-conservative"
	)
	inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
	output = model.generate(**inputs, max_new_tokens=200)
	print(tokenizer.decode(output[0], skip_special_tokens=True))
	```


	## Methodology

	Produced via §4.1.3.4 calibration-aware MoE expert activation count pruning. 300 held-out code examples (148,945 tokens) profiled across all 32 layers × 8 experts. The 2 least-activated experts per layer were removed. The surviving 6 experts per layer are the ones the model actually uses on the calibration domain.

	Activation profile (sample layers):

	\| Layer \| Top experts \| Bottom experts (removed) \|
	\|---\|---\|---\|
	\| Layer 0 \| 5, 2, 3, 4, 0 (35K-49K) \| 1, 6 (~20K) \|
	\| Layer 16 \| 6, 2, 1, 5, 4 (37K-46K) \| 0, 3 (~20K) \|
	\| Layer 31 \| 3, 6, 5, 7, 0 (35K-54K) \| 1, 2 (~20K) \|

	Full methodology in [the sentinel-ai repository](https://github.com/CambrianTech/sentinel-ai). The pipeline ran as `expert-activation-profile → expert-prune → quant → eval` on NVIDIA GeForce RTX 5090.


	<a id="cross-family-anchor-table"></a>
	## Cross-Family Anchor Table

	Same §4.1.3.4 methodology across independently-trained model families.

	\| Row \| Model \| Family \| Experts \| Kept \| PPL \| Status \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| 1 \| [qwen3-coder-30b-a3b](https://huggingface.co/continuum-ai/qwen3-coder-30b-a3b-compacted-19b-256k) \| Qwen3 MoE \| 128 \| 80 \| — \| ✅ Published \|
	\| 2 \| Mixtral 8x7B \| Mixtral \| 8 \| 6 \| 8.97 \| ✅ This model \|
	\| 3 \| Mixtral 8x22B \| Mixtral \| 8 \| 4 \| — \| 🔄 Forging now \|
	\| 4 \| Qwen3.5-35B-A3B \| Qwen3.5 \| TBD \| TBD \| — \| ⬜ Planned \|
	\| 5 \| DeepSeek-V2-Lite \| DeepSeek \| 64 \| 32 \| — \| ⬜ Planned \|


	## Chain of Custody

	Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#hf.co/continuum-ai/mixtral-8x7b-instruct-compacted-conservative/resolve/main/mixtral-8x7b-instruct-compacted-conservative.alloy.json@b26fd7adf36b7c8c). Download the [alloy file](mixtral-8x7b-instruct-compacted-conservative.alloy.json) to verify independently.

	\| What \| Proof \|
	\|------\|-------\|
	\| Model weights \| `sha256:d7f65e31667d9b9bcfd8ca05e796df87bf8b6e59336a34f4703c9d3904e54bd8` \|
	\| Alloy hash \| `sha256:b26fd7adf36b7c8c` \|
	\| Forged on \| NVIDIA GeForce RTX 5090, 2026-04-10 \|
	\| Trust level \| [`self-attested`](https://github.com/CambrianTech/forge-alloy/blob/main/docs/ATTESTATION.md) \|
	\| Spec \| [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) — Rust/Python/TypeScript \|


	## Make Your Own

	Forged with [Continuum](https://github.com/CambrianTech/continuum) — a distributed AI world that runs on your hardware.

	<p align="center">
	<a href="https://github.com/CambrianTech/continuum"><img src="https://raw.githubusercontent.com/CambrianTech/continuum/main/docs/images/factory.png" alt="Continuum Factory" width="600"/></a>
	</p>

	<p align="center">
	<a href="https://github.com/CambrianTech/continuum"><b>Continuum</b></a> · <a href="https://github.com/CambrianTech/forge-alloy"><b>Forge-Alloy</b></a> · <a href="https://github.com/CambrianTech/sentinel-ai"><b>Sentinel-AI</b></a> · <a href="https://github.com/CambrianTech/open-eyes"><b>Open-Eyes</b></a> · <a href="https://discord.gg/arfbCV2H"><b>Discord</b></a> · <a href="https://www.moltbook.com/u/continuum"><b>Moltbook</b></a>
	</p>

	---

	<div align="center">

	Intelligence for everyone. Exploitation for no one.

	</div>