Instructions to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-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/Llama-2-7b-AQLM-2Bit-8x8-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf

SGLang

How to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-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/Llama-2-7b-AQLM-2Bit-8x8-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/Llama-2-7b-AQLM-2Bit-8x8-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/Llama-2-7b-AQLM-2Bit-8x8-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/Llama-2-7b-AQLM-2Bit-8x8-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf
```

Upgrade format of this model?

by jaemzfleming - opened Mar 1, 2024

Discussion

jaemzfleming

Mar 1, 2024

Hello Andrei, I work for NeuralMagic and I'm adding AQLM support to vLLM in an upcoming PR. Your llama 2 7b 1x16 and 2x8 models have no custom code and a quantization_config block in the config.json which is perfect. I'm able to run those models (and a tiny llama2 you have as well) end to end with no problems.

But this model, and the rest referenced in the readme have what look like an older format with a custom aqlm block in the config.json and custom code, making them not readable by vLLM. I was wondering, do you have plans to update those to the same standard as the first two? Or is that something I could try to do with a PR (if it's just a question of changing the config.json and removing the custom code.)

Thanks, -James

BlackSamorez

IST Austria Distributed Algorithms and Systems Lab org Mar 1, 2024

Indeed, I missed this model when updating checkpoints.
I've updated the format.
Thanks!

BlackSamorez changed discussion status to closed Mar 1, 2024

jaemzfleming

Mar 1, 2024

Thank you!

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