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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- emotion-classification |
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- mental-health |
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- llama-3.1 |
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- unsloth |
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- lora |
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- peft |
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- text-generation |
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base_model: unsloth/Meta-Llama-3.1-8B-Instruct |
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datasets: |
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- google-research-datasets/go_emotions |
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- emotion |
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- cardiffnlp/tweet_eval |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# Fine-Tuned Emotion Classification Model |
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## Model Information |
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- **Base Model**: unsloth/Meta-Llama-3.1-8B-Instruct |
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- **Training Method**: LoRA (Low-Rank Adaptation) |
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- **LoRA Rank**: 32 |
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- **Training Samples**: 56,400 |
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- **Datasets Used**: GoEmotions, Emotion, TweetEval |
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## How to Load This Model |
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```python |
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from unsloth import FastLanguageModel |
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# Load the fine-tuned model |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name="emotion_model_finetuned", |
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max_seq_length=2048, |
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dtype=None, |
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load_in_4bit=True, |
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) |
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# Enable inference mode |
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FastLanguageModel.for_inference(model) |
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# Use the model |
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prompt = """<|im_start|>system |
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You are a compassionate mental health support assistant.<|im_end|> |
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<|im_start|>user |
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I'm feeling anxious about tomorrow.<|im_end|> |
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<|im_start|>assistant |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=128) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Files Included |
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- `adapter_config.json` - LoRA adapter configuration |
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- `adapter_model.safetensors` - Fine-tuned weights |
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- `tokenizer.json` - Tokenizer files |
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- `training_config.json` - Training hyperparameters |
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