TinyLlama Fine-tuned for Function Calling
This is a fine-tuned version of the TinyLlama model optimized for function calling tasks.
Model Details
- Base Model: Successmove/tinyllama-function-calling-cpu-optimized
- Fine-tuning Data: Successmove/combined-function-calling-context-dataset
- Training Method: LoRA (Low-Rank Adaptation)
- Training Epochs: 3
- Final Training Loss: ~0.05
Usage
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.