Instructions to use fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym"
# 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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym

SGLang

How to use fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym 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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym" \
    --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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym",
		"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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym" \
        --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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym
```

iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym / README.md

fbaldassarri

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	---
	language:
	- it
	tags:
	- pretrained
	- pytorch
	- causal-lm
	- autoround
	- intel-autoround
	- woq
	- gptq
	- autogptq
	- auto-gptq
	- intel
	- italia
	- italiano
	- italian
	license: mit
	license_link: https://huggingface.co/iGeniusAI/Italia-9B-Instruct-v0.1/blob/main/LICENSE
	model_name: Italia 9B Instruct v0.1
	base_model:
	- iGeniusAI/Italia-9B-Instruct-v0.1
	inference: false
	model_creator: iGeniusAI
	pipeline_tag: text-generation
	prompt_template: '{prompt}
	'
	quantized_by: fbaldassarri
	---

	## Model Information

	Quantized version of [iGeniusAI/Italia-9B-Instruct-v0.1](https://huggingface.co/iGeniusAI/Italia-9B-Instruct-v0.1) using torch.float32 for quantization tuning.
	- 8 bits (INT8)
	- group size = 128
	- Asymmetrical Quantization
	- Method AutoGPTQ

	Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.6

	Note: this INT8 version of Italia-9B-Instruct-v0.1 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, GPTNeoXModel
	model_name = "iGeniusAI/Italia-9B-Instruct-v0.1"
	model = GPTNeoXModel.from_pretrained(model_name, trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	from auto_round import AutoRound
	bits, group_size, sym, device, amp = 8, 128, False, '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/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-asym"
	autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
	```

	Note: the `GPTNeoXSdpaAttention` class is deprecated in favor of simply modifying the `config._attn_implementation`attribute of the `GPTNeoXAttention` class. So this require transformers<4.48.

	## License

	[MIT](https://huggingface.co/iGeniusAI/Italia-9B-Instruct-v0.1/blob/main/LICENSE)

	## Disclaimer

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

	## Potential Error

	Error on Layer 138: auto-gptq format may not support loading this quantized model.