Instructions to use keyfan/Mixtral-8x7B-Instruct-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keyfan/Mixtral-8x7B-Instruct-2bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="keyfan/Mixtral-8x7B-Instruct-2bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("keyfan/Mixtral-8x7B-Instruct-2bit")
model = AutoModelForCausalLM.from_pretrained("keyfan/Mixtral-8x7B-Instruct-2bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keyfan/Mixtral-8x7B-Instruct-2bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keyfan/Mixtral-8x7B-Instruct-2bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keyfan/Mixtral-8x7B-Instruct-2bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/keyfan/Mixtral-8x7B-Instruct-2bit

SGLang

How to use keyfan/Mixtral-8x7B-Instruct-2bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "keyfan/Mixtral-8x7B-Instruct-2bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keyfan/Mixtral-8x7B-Instruct-2bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "keyfan/Mixtral-8x7B-Instruct-2bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keyfan/Mixtral-8x7B-Instruct-2bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use keyfan/Mixtral-8x7B-Instruct-2bit with Docker Model Runner:
```
docker model run hf.co/keyfan/Mixtral-8x7B-Instruct-2bit
```

This is 2-bit quantization of mistralai/Mixtral-8x7B-Instruct-v0.1 using QuIP#

Model loading

Please follow the instruction of QuIP-for-all for usage.

As an alternative, you can use vLLM branch for faster inference. QuIP has to launch like 5 kernels for each linear layer, so it's very helpful for vLLM to use cuda-graph to reduce launching overhead. BTW, If you have problem installing fast-hadamard-transform from pip, you can also install it from source

Perplexity

Measured at Wikitext with 4096 context length

fp16	2-bit
3.8825	5.2799

Speed

Measured with examples/benchmark_latency.py script at vLLM repo. At batch size = 1, it generates at 16.3 tokens/s with single 3090.

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