Instructions to use fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym

SGLang

How to use fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym 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 "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym" \
    --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": "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym",
		"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 "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym" \
        --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": "fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym
```

openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym / README.md

fbaldassarri

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9df9a72 verified about 1 year ago

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metadata

tags:
  - pytorch
  - causal-lm
  - OpenLLaMA
  - autoround
  - auto-round
  - intel-autoround
  - gptq
  - auto-gptq
  - autogptq
  - woq
  - intel
  - pytorch
  - openlm-research
license: apache-2.0
datasets:
  - tiiuae/falcon-refinedweb
  - bigcode/starcoderdata
  - togethercomputer/RedPajama-Data-1T
model_name: OpenLLaMA 3B v2
base_model:
  - openlm-research/open_llama_3b_v2
inference: false
model_creator: openlm-research
pipeline_tag: text-generation
prompt_template: '{prompt} '
quantized_by: fbaldassarri

Model Information

Quantized version of openlm-research/open_llama_3b_v2 using torch.float32 for quantization tuning.

4 bits (INT4)
group size = 128
Symmetrical Quantization
AutoGPTQ format

Quantization framework: Intel AutoRound v0.4.6

Note: this INT4 version of open_llama_3b_v2 has been quantized to run inference through CPU.

Replication Recipe

Step 1 Install Requirements

I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.

wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.6.tar.gz
tar -xvzf v0.4.6.tar.gz
cd auto-round-0.4.6
pip install -r requirements-cpu.txt --upgrade

Step 2 Build Intel AutoRound wheel from sources

pip install -vvv --no-build-isolation -e .[cpu]

Step 3 Script for Quantization

  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "openlm-research/open_llama_3b_v2"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym, device, amp = 4, 128, True, 'cpu', False
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
  autoround.quantize()
  output_dir = "./AutoRound/openlm-research_open_llama_3b_v2-autogptq-int4-gs128-sym"
  autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)

License

Apache 2.0 License

Disclaimer

This quantized model comes with no warranty. It has been developed only for research purposes.