LFM2.5-1.2B-Text2SQL (MLX)
A fine-tuned version of LiquidAI/LFM2.5-1.2B-Instruct for Text-to-SQL generation.
Model Description
This model was fine-tuned on 2000 synthetic Text-to-SQL examples generated using a teacher model (DeepSeek V3). The fine-tuning was performed using LoRA adapters with MLX on Apple Silicon, then fused into the base model.
Training Details
- Base Model: LiquidAI/LFM2.5-1.2B-Instruct
- Training Data: 2000 synthetic examples
- Training Method: LoRA fine-tuning (FP16)
- Iterations: 5400
- Hardware: Apple Silicon (MLX)
Performance
Model Comparison
| Metric | Teacher (DeepSeek V3) | Base Model | Fine-tuned |
|---|---|---|---|
| Exact Match | 60% | 48% | 72% |
| LLM-as-Judge | 90% | 75% | 87% |
| ROUGE-L | 92% | 83% | 94% |
| BLEU | 85% | 70% | 89% |
| Semantic Similarity | 96% | 93% | 97% |
Training Progression
The model shows consistent improvement across all checkpoints with no signs of overfitting.
Usage
MLX (Apple Silicon)
from mlx_lm import load, generate
model, tokenizer = load("hybridaione/LFM2.5-1.2B-Text2SQL-MLX")
# Example query
prompt = '''CREATE TABLE employees (id INT, name VARCHAR, salary DECIMAL);
Question: What are the names of employees earning more than 50000?'''
response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)
License
This model is released under the Apache 2.0 license, following the base model's license.
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Model size
1B params
Tensor type
BF16
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Hardware compatibility
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Base model
LiquidAI/LFM2.5-1.2B-Base Finetuned
LiquidAI/LFM2.5-1.2B-Instruct
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("hybridaione/LFM2.5-1.2B-Text2SQL-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True)