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astom-M/matsuo-llm-advanced-dbbench-bf16

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face
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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

ParameterValue
LoRA rank (r)8
LoRA alpha8
LoRA dropout0
Target modulesqproj, kproj, vproj, oproj, gateproj, upproj, down_proj
Trainable params~0.14% of total

Training Hyperparameters

ParameterValue
Learning rate2e-5
Epochs0.3
Batch size (effective)16 (1 × 16 grad accum)
Max sequence length4096
LR schedulercosine
OptimizerAdamW 8-bit
Warmup steps10
Weight decay0.01
Precisionbfloat16

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

python
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

bash
python -m vllm.entrypoints.openai.api_server \
    --model astom-M/matsuo-llm-advanced-dbbench-bf16 \
    --dtype bfloat16 \
    --max-model-len 4096

Important 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.json does NOT contain quantization_config — clean bf16 model.
  • —All safetensor weights are in torch.bfloat16 dtype.

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.