Instructions to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym")
model = AutoModelForCausalLM.from_pretrained("fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Transformers.js

How to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym

SGLang

How to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-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/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym
```

Model Information

Quantized version of HuggingFaceTB/SmolLM2-1.7B-Instruct using torch.float32 for quantization tuning.

4 bits (INT4)
group size = 128
Asymmetrical Quantization
Method AutoGPTQ

Quantization framework: Intel AutoRound

Note: this INT4 version of SmolLM2-1.7B-Instruct 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.

python -m pip install <package> --upgrade

accelerate==1.0.1
auto_gptq==0.7.1
neural_compressor==3.1
torch==2.3.0+cpu
torchaudio==2.5.0+cpu
torchvision==0.18.0+cpu
transformers==4.45.2

Step 2 Build Intel Autoround wheel from sources

python -m pip install git+https://github.com/intel/auto-round.git

Step 3 Script for Quantization

  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym = 4, 128, False
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
  autoround.quantize()
  output_dir = "./AutoRound/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_gptq-int4-gs128-asym"
  autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)