Instructions to use oretti/db-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use oretti/db-merged with PEFT:
```
Task type is invalid.
```

How to use oretti/db-merged with Transformers:

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

pipe = pipeline("text-generation", model="oretti/db-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oretti/db-merged")
model = AutoModelForCausalLM.from_pretrained("oretti/db-merged", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oretti/db-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/oretti/db-merged

SGLang

How to use oretti/db-merged 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 "oretti/db-merged" \
    --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": "oretti/db-merged",
		"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 "oretti/db-merged" \
        --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": "oretti/db-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use oretti/db-merged with Docker Model Runner:
```
docker model run hf.co/oretti/db-merged
```

db-merged / README.md

oretti

upload merged model (fixed base_model metadata)

5eb2240 verified 9 months ago

preview code

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metadata

license: apache-2.0
base_model: oretti/merged_qwen3_4b_1
datasets:
  - u-10bei/dbbench_sft_dataset_react_v4
tags:
  - peft
  - lora
  - sft
  - transformers
  - 4bit

Fine-tuned model (merged)

Base model: oretti/merged_qwen3_4b_1
Dataset: u-10bei/dbbench_sft_dataset_react_v4
Method: SFT (assistant-only loss)
Format: Merged full model

Training config (key)

max_seq_len: 1024
epochs: 2
per_device_train_bs: 1
grad_accum: 4
lr: 1e-05
warmup_ratio: 0.1
weight_decay: 0.05
lora_r: 64
lora_alpha: 128
lora_dropout: 0.0
target_modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj

Generated by train_sft.py.