| --- |
| license: mit |
| tags: |
| - llm |
| - tinyllama |
| - function-calling |
| - question-answering |
| - finetuned |
| --- |
| |
| # TinyLlama Fine-tuned for Function Calling |
|
|
| This is a fine-tuned version of the [TinyLlama](https://huggingface.co/jzhang38/TinyLlama) model optimized for function calling tasks. |
|
|
| ## Model Details |
|
|
| - **Base Model**: [Successmove/tinyllama-function-calling-cpu-optimized](https://huggingface.co/Successmove/tinyllama-function-calling-cpu-optimized) |
| - **Fine-tuning Data**: [Successmove/combined-function-calling-context-dataset](https://huggingface.co/datasets/Successmove/combined-function-calling-context-dataset) |
| - **Training Method**: LoRA (Low-Rank Adaptation) |
| - **Training Epochs**: 3 |
| - **Final Training Loss**: ~0.05 |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| # Load base model |
| base_model_name = "Successmove/tinyllama-function-calling-cpu-optimized" |
| model = AutoModelForCausalLM.from_pretrained(base_model_name) |
| |
| # Load the LoRA adapters |
| model = PeftModel.from_pretrained(model, "path/to/this/model") |
| |
| # Load tokenizer |
| tokenizer = AutoTokenizer.from_pretrained("path/to/this/model") |
| |
| # Generate text |
| input_text = "Set a reminder for tomorrow at 9 AM" |
| inputs = tokenizer(input_text, return_tensors="pt") |
| outputs = model.generate(**inputs, max_new_tokens=100) |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| ``` |
|
|
| ## Training Details |
|
|
| This model was fine-tuned using: |
| - LoRA with r=8 |
| - Learning rate: 2e-4 |
| - Batch size: 4 |
| - Gradient accumulation steps: 2 |
| - 3 training epochs |
|
|
| ## Limitations |
|
|
| This is a research prototype and may not be suitable for production use without further evaluation and testing. |
|
|
| ## License |
|
|
| This model is licensed under the MIT License. |
