Upload README.md

README.md

@@ -1,3 +1,91 @@
----
-license: llama3.2
----
+---
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+license: llama3.2
+library_name: transformers
+tags:
+- autoround
+- intel
+- gptq
+- woq
+- meta
+- pytorch
+- llama
+- llama-3
+model_name: Llama 3.2 3B Instruct
+base_model: meta-llama/Llama-3.2-3B-Instruct
+inference: false
+model_creator: meta-llama
+pipeline_tag: text-generation
+prompt_template: '{prompt}
+  '
+quantized_by: fbaldassarri
+---
+
+## Model Information
+
+Quantized version of [meta-llama/Llama-3.2-3B-Instruct](meta-llama/Llama-3.2-3B-Instruct) using torch.float32 for quantization tuning.
+- 4 bits (INT4)
+- group size = 128
+- Asymmetrical Quantization
+
+Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128)
+
+Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
+
+Note: this INT4 version of Llama-3.2-3B-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 = "meta-llama/Llama-3.2-3B-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/meta-llama_Llama-3.2-3B-Instruct-auto_round-int4-gs128-asym"
+  autoround.save_quantized(output_dir, format='auto_round', inplace=True)
+```
+
+## License
+
+[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+
+## Disclaimer
+
+This quantized model comes with no warrenty. It has been developed only for research purposes. 
+