Llama-3.2-1B-Instruct-ov-INT4
- Model creator: Meta Llama
- Original model: Llama-3.2-1B-Instruct
Description
This is Llama-3.2-1B-Instruct model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.
Quantization Parameters
Weight compression was performed using nncf.compress_weights
with the following parameters:
- mode: int4_sym
- ratio: 1
For more information on quantization, check the OpenVINO model optimization guide.
Compatibility
The provided OpenVINO™ IR model is compatible with:
- OpenVINO version 2024.4.0 and higher
- Optimum Intel 1.19.0 and higher
Running Model Inference with Optimum Intel
- Install packages required for using Optimum Intel integration with the OpenVINO backend:
pip install optimum[openvino]
- Run model inference:
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "srang992/Llama-3.2-1B-Instruct-ov-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
For more examples and possible optimizations, refer to the OpenVINO Large Language Model Inference Guide.
Running Model Inference with OpenVINO GenAI
- Install packages required for using OpenVINO GenAI.
pip install openvino-genai huggingface_hub
- Download model from HuggingFace Hub
import huggingface_hub as hf_hub
model_id = "srang992/Llama-3.2-1B-Instruct-ov-INT4"
model_path = "Llama-3.2-1B-Instruct-ov-INT4"
hf_hub.snapshot_download(model_id, local_dir=model_path)
- Run model inference:
import openvino_genai as ov_genai
device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
print(pipe.generate("What is OpenVINO?", max_length=200))
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Model tree for srang992/Llama-3.2-1B-Instruct-ov-INT4
Base model
meta-llama/Llama-3.2-1B-Instruct