初始化项目，由ModelHub XC社区提供模型

Model: farehaheha/llama3.2-3B-text-to-sql
Source: Original Platform

README.md

@@ -0,0 +1,114 @@
+---
+base_model: meta-llama/Llama-3.2-3B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text-to-sql
+  - sql
+  - llama
+  - llama-3.2
+  - qlora
+  - lora
+  - unsloth
+  - gguf
+language:
+  - en
+datasets:
+  - gretelai/synthetic_text_to_sql
+---
+
+# Llama 3.2 3B — Text-to-SQL
+
+A fine-tuned version of **Meta Llama 3.2 3B** for converting natural-language questions into SQL queries.
+
+The model was fine-tuned using **QLoRA** with a 4-bit quantized base model and LoRA adapters. After training, the adapter was merged with the base model to produce this standalone model.
+
+## Model Repositories
+
+### Merged Model
+
+The base model and trained LoRA adapter have been merged into a standalone model:
+
+https://huggingface.co/farehaheha/llama3.2-3B-text-to-sql
+
+### Quantized GGUF Model
+
+A `Q4_K_M` GGUF quantized version is available for efficient local inference with llama.cpp and other GGUF-compatible runtimes:
+
+https://huggingface.co/farehaheha/llama3.2-3B-text-to-sql-Q4_K_M-GGUF
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| Base Model | `meta-llama/Llama-3.2-3B` |
+| Fine-tuning Method | QLoRA |
+| Base Model Quantization During Training | 4-bit |
+| LoRA Rank | 16 |
+| Training Dataset | `gretelai/synthetic_text_to_sql` |
+| Dataset Split Used | Test split |
+| Training Samples | ~5.85K |
+| Epochs | 1 |
+| Batch Size | 8 |
+| Training Time | ~30 minutes |
+
+> **Note:** This experiment used the dataset's test split for fine-tuning rather than the training split. Therefore, the original test split should not be used to report an unbiased evaluation score for this model.
+
+## Dataset
+
+The model was fine-tuned on:
+
+**Gretel AI Synthetic Text-to-SQL**
+
+https://huggingface.co/datasets/gretelai/synthetic_text_to_sql
+
+The dataset contains natural-language questions paired with SQL queries and database context.
+
+Approximately **5,850 examples from the test split** were used for this fine-tuning experiment.
+
+## Fine-Tuning Approach
+
+The training process used QLoRA:
+
+1. The Llama 3.2 3B base model was loaded in 4-bit precision.
+2. The original base model weights remained frozen.
+3. LoRA adapters with rank 16 were trained on the Text-to-SQL dataset.
+4. Training was performed for one epoch.
+5. The trained LoRA adapter was merged with the base model.
+6. The merged model was also converted to Q4_K_M GGUF for local inference.
+
+This approach significantly reduces the memory required for fine-tuning compared with full-parameter training.
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "farehaheha/llama3.2-3B-text-to-sql"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto",
+)
+
+prompt = """### Database Schema:
+{your_database_schema}
+
+### Request:
+{your_natural_language_request}
+
+### SQL Query:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=False,
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)