Instructions to use amayuelas/Qwen3.5-4B-MatRL-MT-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amayuelas/Qwen3.5-4B-MatRL-MT-SFT with Transformers:

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

pipe = pipeline("text-generation", model="amayuelas/Qwen3.5-4B-MatRL-MT-SFT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("amayuelas/Qwen3.5-4B-MatRL-MT-SFT")
model = AutoModelForMultimodalLM.from_pretrained("amayuelas/Qwen3.5-4B-MatRL-MT-SFT", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amayuelas/Qwen3.5-4B-MatRL-MT-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amayuelas/Qwen3.5-4B-MatRL-MT-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amayuelas/Qwen3.5-4B-MatRL-MT-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amayuelas/Qwen3.5-4B-MatRL-MT-SFT

SGLang

How to use amayuelas/Qwen3.5-4B-MatRL-MT-SFT 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 "amayuelas/Qwen3.5-4B-MatRL-MT-SFT" \
    --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": "amayuelas/Qwen3.5-4B-MatRL-MT-SFT",
		"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 "amayuelas/Qwen3.5-4B-MatRL-MT-SFT" \
        --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": "amayuelas/Qwen3.5-4B-MatRL-MT-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amayuelas/Qwen3.5-4B-MatRL-MT-SFT with Docker Model Runner:
```
docker model run hf.co/amayuelas/Qwen3.5-4B-MatRL-MT-SFT
```

Qwen3.5-4B — MatRL multi-turn SFT (cold start)

Qwen/Qwen3.5-4B cold-started on amayuelas/matrl-sft-mt: 1,123 multi-turn tool-use episodes for crystal-structure inverse design.

This is the cold-start checkpoint. Its job is format and the tool loop — propose, evaluate, refine, and above all commit within the turn budget. The chemistry is learned afterwards by RL; see amayuelas/Qwen3.5-4B-MatRL-MT-RL.

Why a cold start is needed

The base model essentially never commits. Across 2,880 multi-turn rollouts it made 3 submit calls — it proposes prolifically (~5.6 candidates/rollout) and evaluates sparingly, then runs out of turns. Strict multi-turn SUN is therefore 0% by construction: a commitment failure, not a chemistry failure. Teaching the agent to close an episode is precisely what this stage installs.

Training


base	`Qwen/Qwen3.5-4B`
data	1,123 episodes, assistant-only loss
steps	423 (3 epochs), global batch 8
seq len	16,384
optimizer	AdamW, lr 2e-5, constant
precision	bf16
parallelism	FSDP + context parallel (cp=2, ulysses)
trainer	prime-rl

Trained on 4×A100-40GB. Final loss ~0.36.

Context parallelism uses ulysses, not ring: Qwen3.5 is a hybrid with linear attention (DeltaNet) layers, and ring attention is a softmax-attention algorithm that does not apply to them.

Important: thinking channel

This model is trained with reasoning in the native thinking channel (reasoning_content → <think>), preserved across tool calls. Do not evaluate it with enable_thinking=false — that disables exactly the behavior this run trains.

Serving

Qwen3.5 is a VL-capable model class, so vLLM requires an image-processor config even for text-only serving. preprocessor_config.json and video_preprocessor_config.json are included; without them vLLM fails on load.

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Safetensors

Model size

5B params

Tensor type

BF16

Model tree for amayuelas/Qwen3.5-4B-MatRL-MT-SFT

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