README.md

metadata

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

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