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 new

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

EnricoFermi commited on Apr 11

Commit

8ec99b9

verified ·

1 Parent(s): 4599e55

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

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@@ -53,13 +53,13 @@ license: apache-2.0
 <p align="center">
-<a href="https://cambriantech.github.io/forge-alloy/verify/#huggingface.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/#huggingface.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>
@@ -160,7 +160,7 @@ Same §4.1.3.4 methodology across independently-trained model families.
 ## Chain of Custody
-Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#huggingface.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 |
 |------|-------|

 <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>
 ## 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 |
 |------|-------|