---
language:
- en
license: apache-2.0
tags:
- emotion-classification
- mental-health
- llama-3.1
- unsloth
- lora
- peft
- text-generation
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
datasets:
- google-research-datasets/go_emotions
- emotion
- cardiffnlp/tweet_eval
library_name: transformers
pipeline_tag: text-generation
---

# Fine-Tuned Emotion Classification Model

## Model Information
- **Base Model**: unsloth/Meta-Llama-3.1-8B-Instruct
- **Training Method**: LoRA (Low-Rank Adaptation)
- **LoRA Rank**: 32
- **Training Samples**: 56,400
- **Datasets Used**: GoEmotions, Emotion, TweetEval

## How to Load This Model

```python
from unsloth import FastLanguageModel

# Load the fine-tuned model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="emotion_model_finetuned",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Use the model
prompt = """<|im_start|>system
You are a compassionate mental health support assistant.<|im_end|>
<|im_start|>user
I'm feeling anxious about tomorrow.<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Files Included
- `adapter_config.json` - LoRA adapter configuration
- `adapter_model.safetensors` - Fine-tuned weights
- `tokenizer.json` - Tokenizer files
- `training_config.json` - Training hyperparameters