Note: this INT4 version of SmolLM2 1.7B Instruct has been quantized for inference on Intel CPU, Intel iGPU (Arc) via intel-extension-for-pytorch, Intel NPU (AI Boost on Core Ultra series) via OpenVINO.

Usage

This is an instruct / chat model — use its built-in chat template rather than raw text prompts:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_awq-int4-gs64-asym"
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(repo)

messages = [{"role": "user", "content": "Hello!"}]
inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Replication Recipe

The recommended way to reproduce this exact quantization is via the auto-round-pipeline — the same orchestration tool that produced this artifact.

Step 1 — Bootstrap the auto-round-pipeline

Set up a dedicated conda environment using the pipeline's setup.sh. Any of the install modes below produces an environment that can reproduce this quantization; pick the one that matches your goals:

git clone https://git.epicdynamic.com/auto-round-pipeline
cd auto-round-pipeline

# Pinned PyPI wheel (fastest; matches what this pipeline used by default):
bash setup.sh --pip-version 0.13.0

# Or build from intel/auto-round at the same tag (byte-identical reproducibility):
bash setup.sh --source-tag v0.13.0

# Intel Arc iGPU acceleration (e.g. Core Ultra 185H) — append to either of the above:
#   ... --intel-xpu
# NVIDIA / AMD opt-in: --cuda / --rocm

The script prints the resulting conda env name (something like auto-round-pipeline-v0.13.0[-src][-xpu|-cuda|-rocm]) at the end.

Step 2 — Quantize just this model

Activate the env that setup.sh created, then invoke the runner with the same job filters that produced this artifact:

conda activate <env-name-printed-by-setup.sh>
python runner.py \
    --model 'HuggingFaceTB/SmolLM2-1.7B-Instruct' \
    --quant 'INT4-gs64' \
    --format auto_awq \
    --no-upload      # drop this to also push to HuggingFace Hub

Step 3 — (Optional) standalone Python recipe

If you'd rather call auto-round directly without the orchestration wrapper, this is the exact call the pipeline made:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound

model_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)

bits, group_size, sym = 4, 64, False
autoround = AutoRound(
    model, tokenizer,
    bits=bits, group_size=group_size, sym=sym,
    device_map="cpu",
    nsamples=128, iters=200, seqlen=512, batch_size=4,
)
autoround.quantize_and_save("./AutoRound/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_awq-int4-gs64-asym", format="auto_awq")

Actual Run Conditions

Recorded by the auto-round-pipeline at quantization time:

Field	Value
Intel auto-round version	0.13.0
transformers version	4.55.3
torch version	2.12.0+cpu
torch_dtype (load)	torch.bfloat16
calibration device	`cpu`
calibration samples	128
tuning iterations	200
calibration seq len	512
calibration batch size	4
quantization duration	13542.8s (225.7 min)
completed at (UTC)	2026-06-24T04:31:47.309689+00:00

License

Apache 2.0 License

Disclaimer

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