metadata

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 using optimum-intel via the 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:

pip install optimum[openvino]

To load your model you can do as follows:

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

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])