Instructions to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-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-2x8-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-2x8-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-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-2x8-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-2x8-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-2x8-hf

SGLang

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

BlackSamorez commited on Mar 11, 2024

Commit

017ede1

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@@ -6,14 +6,14 @@ 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](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf) |
-| Llama-2-7b† (THIS) | 2x8         | 6.69          | 2.2            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-2x8-hf)  |
-| Llama-2-7b† | 8x8         | 6.61          | 2.2            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-8x8-hf)  |
-| Llama-2-13b| 1x16        | 5.41           | 4.1            | [Link](https://huggingface.co/BlackSamorez/Llama-2-13b-AQLM-2Bit-1x16-hf)|
-| Llama-2-70b| 1x16        | 3.96           | 18.8           | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf)|
-| Llama-2-70b| 2x8         | 4.83           | 18.2           | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-2x8-hf) |
-| Mixtral-8x7b| 1x16       | 4.37           | 12.6            | [Link](https://huggingface.co/BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
-| Mixtral-8x7b-Instruct| 1x16       | -           | 12.6            | [Link](https://huggingface.co/BlackSamorez/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
 **UPD** (20.02.2024).
 We applied global finetuning on top of quantized model and improved results compared to first revision.

 | Model      | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link                                                                 |
 |------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
+| Llama-2-7b | 1x16        | 5.92          | 2.4            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
+| Llama-2-7b (THIS) | 2x8         | 6.69          | 2.2            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf)  |
+| Llama-2-7b | 8x8         | 6.61          | 2.2            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf)  |
+| Llama-2-13b| 1x16        | 5.22           | 4.1            | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
+| Llama-2-70b| 1x16        | 3.83           | 18.8           | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
+| Llama-2-70b| 2x8         | 4.21           | 18.2           | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
+| Mixtral-8x7b| 1x16       | 3.35           | 12.6            | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
+| Mixtral-8x7b-Instruct| 1x16       | -           | 12.6            | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
 **UPD** (20.02.2024).
 We applied global finetuning on top of quantized model and improved results compared to first revision.