Quantized version of TinyLlama/TinyLlama_v1.1 using torch.bfloat16 for quantization tuning.
- 4 bits (INT4)
- group size = 64
- Asymmetrical Quantization
- Method: WoQ — AWQ (AutoAWQ algorithm)
Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64)
Quantization framework: Intel AutoRound v0.13.0
Note: this INT4 version of TinyLlama_v1.1 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 a base / completion model — prompt it directly with raw text:
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_awq-int4-gs64-asym"
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(repo)
prompt = "The quick brown fox"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
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 'TinyLlama/TinyLlama_v1.1' \
--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:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound
model_name = "TinyLlama/TinyLlama_v1.1"
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/TinyLlama_TinyLlama_v1.1-auto_awq-int4-gs64-asym", format="auto_awq")
Actual Run Conditions
Recorded by the auto-round-pipeline at quantization time:
Table with columns: Field, Value| 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 |
License
Apache 2.0 License
Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
