Instructions to use cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic with Transformers:

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

pipe = pipeline("image-text-to-text", model="cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic")
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("cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic")
model = AutoModelForMultimodalLM.from_pretrained("cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic", 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 cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic",
		"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/cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic

SGLang

How to use cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic 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 "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic" \
    --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": "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic",
		"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 "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic" \
        --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": "cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic",
		"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 cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic
```

JoyFox-Qwen3.6-35B-A3B-RP-Aggressive — FP8 Dynamic

FP8_DYNAMIC (W8A8) quantization of joyfox/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive (architecture Qwen3_5MoeForConditionalGeneration, base model Qwen/Qwen3.6-35B-A3B) — a 35B-A3B MoE roleplay finetune.

Source: joyfox/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive
License: apache-2.0 (inherited from the source and base models)
Tool: llm-compressor, data-free one-shot
Scheme: FP8 per-channel weights (static), FP8 per-token activations (dynamic)
Weights size: 36.66 GiB (down from ~70 GiB BF16) — fits a single 96 GB GPU with ample room for KV cache

Kept in BF16

The following modules were excluded from quantization: lm_head, embeddings, the vision tower (model.visual.*), the Gated DeltaNet linear-attention layers (linear_attn.*), MoE routers (mlp.gate), the shared-expert gate (shared_expert_gate) and the MTP head (mtp.*). The ignore list matches the reference RedHatAI/Qwen3.6-35B-A3B-FP8-dynamic quantization.

Recipe

from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

MODEL_ID = "joyfox/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive"
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(MODEL_ID, dtype="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)

recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=[
        "re:.*lm_head",
        "re:visual.*",
        "re:model.visual.*",
        "re:.*mlp.gate$",
        "re:.*embed_tokens$",
        "re:.*shared_expert_gate$",
        "re:.*linear_attn.*",
        "re:^mtp.*",
    ],
)
oneshot(model=model, recipe=recipe)
model.save_pretrained("out")
processor.save_pretrained("out")

Serving with vLLM

vllm serve cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic \
  --served-model-name joyfox \
  --enable-chunked-prefill \
  --max-model-len 16384 --kv-cache-dtype fp8 \
  --limit-mm-per-prompt '{"image": 0, "audio": 0, "video": 0}' \
  --default-chat-template-kwargs '{"enable_thinking": false}'

Notes:

Chunked prefill is required. The hybrid attention stack (Gated DeltaNet linear-attention layers) uses vLLM's mamba cache in mode align; passing --no-enable-chunked-prefill makes the engine refuse to start.
The MTP head is shipped as model_mtp.safetensors, but speculative decoding is intentionally not used here.
Thinking (<think>) is disabled at the chat-template level via enable_thinking: false.

Downloads last month: 32

Safetensors

Model size

35B params

Tensor type

BF16

F8_E4M3

Model tree for cloud19/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive-FP8-Dynamic

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

joyfox/JoyFox-Qwen3.6-35B-A3B-RP-Aggressive

Quantized

(4)

this model