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---
base_model: Geraldine/FineLlama-3.2-3B-Instruct-ead
library_name: transformers
pipeline_tag: text-generation
tags:
- openvino
- openvino-export
license: llama3.2
---
# FineLlama-3.2-3B-Instruct-ead-openvino
This model was converted to OpenVINO from [`Geraldine/FineLlama-3.2-3B-Instruct-ead`](https://huggingface.co./Geraldine/FineLlama-3.2-3B-Instruct-ead) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co./spaces/echarlaix/openvino-export) space.
## Model Description
- **Original Model**: Geraldine/FineLlama-3.2-3B-Instruct-ead
- **Framework**: OpenVINO
- **Task**: Text Generation, EAD tag generation
- **Language**: English
- **License**: llama3.2
## Features
- Optimized for Intel hardware using OpenVINO
- Supports text generation inference
- Maintains original model capabilities for EAD tag generation
- Integration with PyTorch
## Installation
First make sure you have optimum-intel installed:
```bash
pip install optimum[openvino]
```
To load your model you can do as follows:
```python
from optimum.intel import OVModelForCausalLM
model_id = "Geraldine/FineLlama-3.2-3B-Instruct-ead-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
```
## Technical Specifications
### Supported Features
- Text Generation
- Transformers integration
- PyTorch compatibility
- OpenVINO export
- Inference Endpoints
- Conversational capabilities
### Model Architecture
- Base: meta-llama/Llama-3.2-3B-Instruct
- Fine-tuned: Geraldine/FineLlama-3.2-3B-Instruct-ead
- Final conversion: OpenVINO optimization
## Usage Examples
```python
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer
# Load model and tokenizer
model_id = "Geraldine/FineLlama-3.2-3B-Instruct-ead-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Generate text
def generate_ead(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
return tokenizer.decode(outputs[0])
``` |