Instructions to use tomofusa/exp040-soup-3model-weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tomofusa/exp040-soup-3model-weighted with Transformers:

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

pipe = pipeline("text-generation", model="tomofusa/exp040-soup-3model-weighted")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tomofusa/exp040-soup-3model-weighted")
model = AutoModelForCausalLM.from_pretrained("tomofusa/exp040-soup-3model-weighted", 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 tomofusa/exp040-soup-3model-weighted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tomofusa/exp040-soup-3model-weighted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tomofusa/exp040-soup-3model-weighted",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tomofusa/exp040-soup-3model-weighted

SGLang

How to use tomofusa/exp040-soup-3model-weighted 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 "tomofusa/exp040-soup-3model-weighted" \
    --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": "tomofusa/exp040-soup-3model-weighted",
		"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 "tomofusa/exp040-soup-3model-weighted" \
        --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": "tomofusa/exp040-soup-3model-weighted",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tomofusa/exp040-soup-3model-weighted with Docker Model Runner:
```
docker model run hf.co/tomofusa/exp040-soup-3model-weighted
```

tomofusa commited on Mar 1

Commit

ca7b5b9

verified ·

1 Parent(s): 1effc7b

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,7 +1,8 @@
 ---
 base_model: Qwen/Qwen3-4B-Instruct-2507
 datasets:
-- tomofusa/llm2025-main-blend-h
 - u-10bei/dpo-dataset-qwen-cot
 language:
 - en
@@ -41,8 +42,9 @@ This model is created by **weighted averaging** of 3 independently trained model
 All source models share the same base pipeline:
 1. **Base model**: Qwen/Qwen3-4B-Instruct-2507
-2. **SFT**: QLoRA on [tomofusa/llm2025-main-blend-h](https://huggingface.co/tomofusa/llm2025-main-blend-h) (7,500 samples)
    - SFT adapter: [tomofusa/exp015-blend-h-lora](https://huggingface.co/tomofusa/exp015-blend-h-lora)
    - lr=5e-6, epochs=2, LoRA r=64/alpha=128, max_seq_len=1024
 3. **DPO**: IPO/SimPO on [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot) (4,040 samples)
    - lr=5e-7, beta=0.1, epochs=1, LoRA r=64/alpha=128
@@ -66,6 +68,6 @@ model = AutoModelForCausalLM.from_pretrained(
 ## Sources & Terms
 - **Base model**: [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) - Apache 2.0
-- **SFT data**: [tomofusa/llm2025-main-blend-h](https://huggingface.co/tomofusa/llm2025-main-blend-h) - MIT
 - **DPO data**: [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot)
 - Users must comply with all upstream licenses and terms of use.

 ---
 base_model: Qwen/Qwen3-4B-Instruct-2507
 datasets:
+- daichira/structured-5k-mix-sft
+- daichira/structured-hard-sft-4k
 - u-10bei/dpo-dataset-qwen-cot
 language:
 - en
 All source models share the same base pipeline:
 1. **Base model**: Qwen/Qwen3-4B-Instruct-2507
+2. **SFT**: QLoRA on structured output data (7,500 samples)
    - SFT adapter: [tomofusa/exp015-blend-h-lora](https://huggingface.co/tomofusa/exp015-blend-h-lora)
+   - Sources: [daichira/structured-5k-mix-sft](https://huggingface.co/datasets/daichira/structured-5k-mix-sft) (5,000) + [daichira/structured-hard-sft-4k](https://huggingface.co/datasets/daichira/structured-hard-sft-4k) (2,000 sampled) + custom TOML data (500)
    - lr=5e-6, epochs=2, LoRA r=64/alpha=128, max_seq_len=1024
 3. **DPO**: IPO/SimPO on [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot) (4,040 samples)
    - lr=5e-7, beta=0.1, epochs=1, LoRA r=64/alpha=128
 ## Sources & Terms
 - **Base model**: [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) - Apache 2.0
+- **SFT data**: [daichira/structured-5k-mix-sft](https://huggingface.co/datasets/daichira/structured-5k-mix-sft) (CC-BY-4.0), [daichira/structured-hard-sft-4k](https://huggingface.co/datasets/daichira/structured-hard-sft-4k) (CC-BY-4.0)
 - **DPO data**: [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot)
 - Users must comply with all upstream licenses and terms of use.