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README.md

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+# 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)
+```

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f10c7d5c4d6a534bd3e055aee7715169f58cddf5aafaa526e09ea0b61592aa5
+size 17717352110

training_config.json

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+{
+  "base_model": "mistralai/Mistral-7B-Instruct-v0.3",
+  "finetuning_type": "LoRA",
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "quantize": "bnb.nf4",
+  "context_length": 4098,
+  "train_batch_size": 4,
+  "learning_rate": "2e-4",
+  "train_steps": 2000
+}