---
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
language:
  - en
  - fr
  - es
  - pt
pipeline_tag: text-generation
tags:
- causal-lm
- autoround
- auto-round
- intel-autoround
- woq
- autoawq
- auto-awq
- awq
- intel
- pytorch
- falcon3
model_name: Falcon3 7B Instruct
base_model:
  - tiiuae/Falcon3-7B-Instruct
inference: false
library_name: transformers
model_creator: tiiuae
prompt_template: '{prompt} '
quantized_by: fbaldassarri
---

## Model Information

Quantized version of [tiiuae/Falcon3-7B-Instruct](https://huggingface.co/tiiuae/Falcon3-7B-Instruct) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoAWQ

Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.4

Note: this INT4 version of Falcon3-7B-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. 

```
wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.4.tar.gz
tar -xvzf v0.4.4.tar.gz
cd auto-round-0.4.4
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 = "tiiuae/Falcon3-7B-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/tiiuae_Falcon3-7B-Instruct-autoawq-int4-gs128-asym"
  autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```

## License

[Falcon3 License](https://falconllm.tii.ae/falcon-terms-and-conditions.html)

## Disclaimer

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