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---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: apache-2.0
library_name: transformers
tags:
- autoround
- auto-round
- intel-autoround
- gptq
- woq
- pytorch
- transformers
- intel
model_name: SmolLM2 135M Instruct
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
inference: false
model_creator: HuggingFaceTB
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [HuggingFaceTB/SmolLM2-135M-Instruct](HuggingFaceTB/SmolLM2-135M-Instruct) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method WoQ (AutoRound format)
Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128)
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.3
Note: this INT4 version of SmolLM2-135M-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.
```
wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.3.tar.gz
tar -xvzf v0.4.3.tar.gz
cd auto-round-0.4.3
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
model_name = "HuggingFaceTB/SmolLM2-135M-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 4, 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/HuggingFaceTB_SmolLM2-135M-Instruct-auto_round-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_round', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
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