Instructions to use fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-auto-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-auto-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-auto-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-auto-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-auto-asym

SGLang

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

Model Information

Quantized version of 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 v0.4.6

Note: this INT8 version of Italia-9B-Instruct-v0.1 has been quantized using NVIDIA CUDA libraries.

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.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, 'auto', 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-auto-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_implementationattribute of the GPTNeoXAttention class. So this require transformers<4.48.

License

MIT

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.

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F32

I32

F16

Model tree for fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-autogptq-int8-gs128-auto-asym

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domyn/Italia-9B-Instruct-v0.1

Quantized

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