Instructions to use Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged with Transformers:

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

pipe = pipeline("text-generation", model="Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged")
model = AutoModelForMultimodalLM.from_pretrained("Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged")
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 Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-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": "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged

SGLang

How to use Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-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 "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-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": "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-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 "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-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": "Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged with Docker Model Runner:
```
docker model run hf.co/Laplaces-Red-Devils/physics-v07c-sft-qwen3.5-4b-merged
```

physics-v07c-sft-qwen3.5-4b-merged

v07 SFT of Qwen/Qwen3.5-4B for EXACT-2026 Task-2 physics: emit a short (5-10 line) reasoning preamble then ONE Python code block that computes FINAL ANSWER: / UNIT:. Merged full model — serve directly with vLLM.

Training

Method: 16-bit LoRA, train-on-completion (loss on the assistant turn only).
LoRA: r=8, alpha=16, dropout=0.05, targets=['q_proj', 'k_proj', 'v_proj', 'o_proj'].
Epochs=6, lr=3e-05, eff_batch=16, max_seq_len=2048.
Data: 2634 train / 110 val trajectories (golden_60 held out).
Chat template: Qwen <|im_start|>/<|im_end|>; eos <|im_end|>, pad <|endoftext|>.