Instructions to use shaneMattner/Qwen3.6-35B-A3B-RFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shaneMattner/Qwen3.6-35B-A3B-RFT with Transformers:

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

pipe = pipeline("text-generation", model="shaneMattner/Qwen3.6-35B-A3B-RFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("shaneMattner/Qwen3.6-35B-A3B-RFT")
model = AutoModelForCausalLM.from_pretrained("shaneMattner/Qwen3.6-35B-A3B-RFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shaneMattner/Qwen3.6-35B-A3B-RFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shaneMattner/Qwen3.6-35B-A3B-RFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shaneMattner/Qwen3.6-35B-A3B-RFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shaneMattner/Qwen3.6-35B-A3B-RFT

SGLang

How to use shaneMattner/Qwen3.6-35B-A3B-RFT 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 "shaneMattner/Qwen3.6-35B-A3B-RFT" \
    --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": "shaneMattner/Qwen3.6-35B-A3B-RFT",
		"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 "shaneMattner/Qwen3.6-35B-A3B-RFT" \
        --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": "shaneMattner/Qwen3.6-35B-A3B-RFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shaneMattner/Qwen3.6-35B-A3B-RFT with Docker Model Runner:
```
docker model run hf.co/shaneMattner/Qwen3.6-35B-A3B-RFT
```

Qwen3.6-35B-A3B-RFT / merge_metadata.json

shaneMattner

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	{
	"experiment": "ssd-Qwen3.6-35B-A3B-r16-full-20260505-184155",
	"base_model": "Qwen/Qwen3.6-35B-A3B",
	"adapter_path": "/checkpoints/experiments/ssd-Qwen3.6-35B-A3B-r16-full-20260505-184155/final_adapter",
	"merged_at": "2026-05-06T07:14:33",
	"dtype": "bfloat16",
	"merge_method": "PeftModel.merge_and_unload()",
	"training_metadata": {
	"experiment": "ssd-Qwen3.6-35B-A3B-r16-full-20260505-184155",
	"model": "Qwen/Qwen3.6-35B-A3B",
	"config": {
	"model_name": "Qwen/Qwen3.6-35B-A3B",
	"max_seq_length": 2048,
	"load_in_4bit": false,
	"load_in_8bit": false,
	"lora_r": 16,
	"lora_alpha": 16,
	"lora_dropout": 0.0,
	"lora_bias": "none",
	"use_rslora": false,
	"optim": "adamw_8bit",
	"batch_size": 4,
	"gradient_accumulation_steps": 8,
	"packing": false,
	"learning_rate": 0.0002,
	"lr_scheduler_type": "cosine",
	"warmup_steps": 0.06,
	"weight_decay": 0.01,
	"max_steps": 150,
	"save_steps": 50,
	"eval_steps": 25,
	"logging_steps": 5,
	"seed": 42,
	"experiment_name": "ssd-Qwen3.6-35B-A3B-r16-full-20260505-184155",
	"skip_eval": false,
	"smoke_test": false
	},
	"results": {
	"train_loss": 0.5228066476186116,
	"steps": 150,
	"epoch": null
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	"timings": {
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	"save_count": 3,
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	}
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	}