Instructions to use Yano/exp-0216-005-db-balanced-qwen2.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yano/exp-0216-005-db-balanced-qwen2.5-7b with Transformers:

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

pipe = pipeline("text-generation", model="Yano/exp-0216-005-db-balanced-qwen2.5-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yano/exp-0216-005-db-balanced-qwen2.5-7b")
model = AutoModelForCausalLM.from_pretrained("Yano/exp-0216-005-db-balanced-qwen2.5-7b")
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 Yano/exp-0216-005-db-balanced-qwen2.5-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yano/exp-0216-005-db-balanced-qwen2.5-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yano/exp-0216-005-db-balanced-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yano/exp-0216-005-db-balanced-qwen2.5-7b

SGLang

How to use Yano/exp-0216-005-db-balanced-qwen2.5-7b 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 "Yano/exp-0216-005-db-balanced-qwen2.5-7b" \
    --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": "Yano/exp-0216-005-db-balanced-qwen2.5-7b",
		"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 "Yano/exp-0216-005-db-balanced-qwen2.5-7b" \
        --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": "Yano/exp-0216-005-db-balanced-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Yano/exp-0216-005-db-balanced-qwen2.5-7b with Docker Model Runner:
```
docker model run hf.co/Yano/exp-0216-005-db-balanced-qwen2.5-7b
```

exp-0216-005-db-balanced-qwen2.5-7b

Fine-tuned from Yano/exp-0212-001-alfworld-qwen2.5-7b (001 ALFWorld SFT model) using QLoRA (4-bit, Unsloth).

Purpose

DB Bench training with balanced data (v1-v4 mixed, INSERT/UPDATE downsampled). Addresses 004's SELECT degradation (76.5% -> 41.0%) caused by INSERT/UPDATE data imbalance.

Data Strategy

Combined all DB Bench v1-v4 (3,060 total)
Downsampled INSERT to 200, UPDATE to 150
Kept all SELECT/query-type samples
Final dataset: ~1470 samples (INSERT+UPDATE ~24.5%)

Training Configuration

Base model: Yano/exp-0212-001-alfworld-qwen2.5-7b
Method: QLoRA (4-bit), merged to 16-bit
Max sequence length: 2048
Epochs: 3
Learning rate: 2e-05
LoRA: r=64, alpha=128
Batch size: 2 (grad accum 16)
Warmup ratio: 0.1
Collator: AllAssistantTurnsCollator (all turns supervised)

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Yano/exp-0216-005-db-balanced-qwen2.5-7b

Base model

Yano/exp-0212-001-alfworld-qwen2.5-7b

Adapter

(5)

this model

Adapters

1 model

Yano
/

exp-0216-005-db-balanced-qwen2.5-7b

exp-0216-005-db-balanced-qwen2.5-7b

Purpose

Data Strategy

Training Configuration

Model tree for Yano/exp-0216-005-db-balanced-qwen2.5-7b

Datasets used to train Yano/exp-0216-005-db-balanced-qwen2.5-7b