| # aaa-2-sql | |
| This is a finetuned version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) using LoRA with LitGPT. | |
| ## Training Details | |
| - **Base Model:** mistralai/Mistral-7B-Instruct-v0.3 | |
| - **Framework:** LitGPT | |
| - **Finetuning Method:** Low-Rank Adaptation (LoRA) | |
| - **LoRA Parameters:** | |
| - Rank (r): 16 | |
| - Alpha: 32 | |
| - Dropout: 0.05 | |
| - **Quantization:** bnb.nf4 | |
| - **Context Length:** 4098 tokens | |
| - **Training Steps:** 2000 | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load model and tokenizer | |
| model = AutoModelForCausalLM.from_pretrained("exaler/aaa-2-sql") | |
| tokenizer = AutoTokenizer.from_pretrained("exaler/aaa-2-sql") | |
| # Create prompt | |
| prompt = "Your prompt here" | |
| # Generate text | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| output = model.generate(**inputs, max_new_tokens=1024) | |
| response = tokenizer.decode(output[0], skip_special_tokens=True) | |
| print(response) | |
| ``` | |