astom-M/matsuo-llm-advanced-dbbench-bf16
015
Qwen2.5-7B-Instruct + DBBench bf16 LoRA
Model Description
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct for SQL generation tasks (DBBench).
Key characteristics:
- Base model: Qwen2.5-7B-Instruct
- Training method: bf16 LoRA (NOT QLoRA 4-bit) — zero rounding errors during merge
- Format: bfloat16 safetensors (no quantization)
- Size: ~15GB (9 shards)
- Compatible with: vLLM v0.13.0+, transformers, etc.
Training Details
LoRA Configuration
Training Hyperparameters
Training Data
- Dataset: Multi-turn SQL conversation data (7,490 samples)
- Sources: Spider train + BIRD mini_dev
- Patterns: Direct query (40%), Exploration (30%), Correction (30%)
- Template-based generation (no LLM used for data synthesis)
Training Results
- Steps: 127
- Training time: 6.7 minutes (RTX 5090)
- Train loss: avg 1.30 (start ~2.0, end ~0.69)
- Eval loss: 0.709
- Peak VRAM: 19.0GB / 32GB
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"astom-M/matsuo-llm-advanced-dbbench-bf16",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"astom-M/matsuo-llm-advanced-dbbench-bf16",
trust_remote_code=True
)vLLM
python -m vllm.entrypoints.openai.api_server \
--model astom-M/matsuo-llm-advanced-dbbench-bf16 \
--dtype bfloat16 \
--max-model-len 4096Important Notes
- No quantization artifacts: This model was trained in bf16 full precision (not QLoRA 4-bit), so there are no rounding errors from quantization-to-bf16 merge.
config.jsondoes NOT containquantization_config— clean bf16 model.- All safetensor weights are in
torch.bfloat16dtype.
Compliance
- Base model: Qwen2.5-7B-Instruct (Apache 2.0 license, whitelisted for competition)
- Training data: Template-based synthetic data (no LLM-generated content)
- No inference code modification
- No RAG/ToolUse
- No commercial API usage
License
This model inherits the Apache 2.0 license from Qwen2.5-7B-Instruct.
