Instructions to use astom-M/matsuo-llm-advanced-dbbench-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use astom-M/matsuo-llm-advanced-dbbench-bf16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="astom-M/matsuo-llm-advanced-dbbench-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("astom-M/matsuo-llm-advanced-dbbench-bf16")
model = AutoModelForCausalLM.from_pretrained("astom-M/matsuo-llm-advanced-dbbench-bf16", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use astom-M/matsuo-llm-advanced-dbbench-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "astom-M/matsuo-llm-advanced-dbbench-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astom-M/matsuo-llm-advanced-dbbench-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/astom-M/matsuo-llm-advanced-dbbench-bf16

SGLang

How to use astom-M/matsuo-llm-advanced-dbbench-bf16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "astom-M/matsuo-llm-advanced-dbbench-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astom-M/matsuo-llm-advanced-dbbench-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "astom-M/matsuo-llm-advanced-dbbench-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astom-M/matsuo-llm-advanced-dbbench-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use astom-M/matsuo-llm-advanced-dbbench-bf16 with Docker Model Runner:
```
docker model run hf.co/astom-M/matsuo-llm-advanced-dbbench-bf16
```

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

Parameter	Value
LoRA rank (r)	8
LoRA alpha	8
LoRA dropout	0
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	~0.14% of total

Training Hyperparameters

Parameter	Value
Learning rate	2e-5
Epochs	0.3
Batch size (effective)	16 (1 × 16 grad accum)
Max sequence length	4096
LR scheduler	cosine
Optimizer	AdamW 8-bit
Warmup steps	10
Weight decay	0.01
Precision	bfloat16

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 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.

Downloads last month: 15

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for astom-M/matsuo-llm-advanced-dbbench-bf16

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2724)

this model