Instructions to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf

SGLang

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf 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 "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf
```

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Check out the documentation for more information.

Official AQLM quantization of mistralai/Mixtral-8x7B-v0.1.

For this quantization, we used 1 codebook of 16 bits.

Selected evaluation results for this and other models:

Model	AQLM scheme	WikiText 2 PPL	Model size, Gb	Hub link
Llama-2-7b	1x16	5.92	2.4	Link
Llama-2-7b	2x8	6.69	2.2	Link
Llama-2-7b	8x8	6.61	2.2	Link
Llama-2-13b	1x16	5.22	4.1	Link
Llama-2-70b	1x16	3.83	18.8	Link
Llama-2-70b	2x8	4.21	18.2	Link
Mixtral-8x7b (THIS)	1x16	3.35	12.6	Link
Mixtral-8x7b-Instruct	1x16	-	12.6	Link

To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the official GitHub repo.

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Safetensors

Model size

7B params

Tensor type

F16

I16

Collection including ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf

AQLM

Collection

AQLM quantized LLMs • 21 items • Updated Feb 18 • 46

Paper for ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf

Extreme Compression of Large Language Models via Additive Quantization

Paper • 2401.06118 • Published Jan 11, 2024 • 14