Instructions to use willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034 with Transformers:

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

pipe = pipeline("image-text-to-text", model="willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034")
model = AutoModelForMultimodalLM.from_pretrained("willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034

SGLang

How to use willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034 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 "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034" \
    --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": "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034" \
        --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": "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034 with Docker Model Runner:
```
docker model run hf.co/willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034
```

Qwen3.6-35B-A3B MILES MultiEvo v2 — iteration 34

Reinforcement-learning checkpoint from the miles-multievo-v2 run on Qwen3.6-35B-A3B, taken at training iteration 34. Checkpoints are saved every 5 iterations; the published iterations of this run live together in one collection so you can compare points along the training curve.


Base model	`Qwen3.6-35B-A3B` (`Qwen3_5MoeForConditionalGeneration`)
Stage	RL (MultiEvo v2)
Iteration	34
Architecture	40 layers, hidden 2048, 256 experts (top-8), hybrid linear/full attention, 1 MTP layer, vision tower
Vocab	248320
Precision	bfloat16

Usage

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

repo = "willamazon1/qwen3.6-35b-a3b-miles-multievo-v2-iter034"
proc = AutoProcessor.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")

Conversion

Converted from a Megatron-LM torch_dist training checkpoint to HuggingFace safetensors with slime's tools/convert_torch_dist_to_hf.py, using --vocab-size 248320 to strip embedding padding and -a/--add-missing-from-origin-hf so the vision tower — which the language-model-only torch_dist checkpoint does not carry — is taken from the base model. MoE experts are stored in the grouped/fused layout (mlp.experts.gate_up_proj / down_proj).

Every shard was checked for NaN/Inf and the full tensor key set was diffed against a known-good conversion of this architecture before upload.

Downloads last month: 9

Safetensors

Model size

36B params

Tensor type

F32

BF16

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