Instructions to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-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/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym

SGLang

How to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-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/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym",
		"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 "fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym
```

sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym / README.md

fbaldassarri

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fbd77ce verified over 1 year ago

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	---
	language:
	- it
	- en
	tags:
	- pretrained
	- pytorch
	- causal-lm
	- minerva
	- autoround
	- intel-autoround
	- autoawq
	- auto-awq
	- auto_awq
	- woq
	- gptq
	- intel
	license: apache-2.0
	model_name: Minerva 7B instruct v1.0
	base_model:
	- sapienzanlp/Minerva-7B-instruct-v1.0
	inference: false
	model_creator: sapienzanlp
	datasets:
	- uonlp/CulturaX
	pipeline_tag: text-generation
	prompt_template: '{prompt}
	'
	quantized_by: fbaldassarri
	---

	## Model Information

	Quantized version of [sapienzanlp/Minerva-7B-instruct-v1.0](https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0) using torch.float32 for quantization tuning.
	- 4 bits (INT4)
	- group size = 128
	- Symmetrical Quantization
	- Method AutoAWQ

	Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.3

	Note: this INT4 version of Minerva-7B-instruct-v1.0 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/v0.4.3.tar.gz
	tar -xvzf v0.4.3.tar.gz
	cd auto-round-0.4.3
	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 = "sapienzanlp/Minerva-7B-instruct-v1.0"
	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/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym"
	autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
	```

	## License

	[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)

	## Disclaimer

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