Instructions to use Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Jackrong/Qwopus3.6-35B-A3B-Coder-FP8")
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("Jackrong/Qwopus3.6-35B-A3B-Coder-FP8")
model = AutoModelForMultimodalLM.from_pretrained("Jackrong/Qwopus3.6-35B-A3B-Coder-FP8")
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 Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/Qwopus3.6-35B-A3B-Coder-FP8

SGLang

How to use Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 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 "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8" \
    --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": "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8",
		"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 "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8" \
        --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": "Jackrong/Qwopus3.6-35B-A3B-Coder-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/Qwopus3.6-35B-A3B-Coder-FP8",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jackrong/Qwopus3.6-35B-A3B-Coder-FP8 with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwopus3.6-35B-A3B-Coder-FP8
```

Jackrong commited on 19 days ago

Commit

92d6813

verified ·

1 Parent(s): 681ff6d

Fix FP8 README validation note

Browse files

Files changed (1) hide show

README.md +18 -1

README.md CHANGED Viewed

@@ -431,4 +431,21 @@ The training and evaluation philosophy for this release centers on agent executi
 ## Release variant
-Fine-grained FP8 E4M3 vLLM-compatible release of  using the official Qwen3.6-35B-A3B FP8 quantization format. Local vLLM smoke and 30-question checks were run before upload; answer-only QA passed with no empty answers, no binary/unicode replacement garbage, and no max-token hits.

 ## Release variant
+Fine-grained FP8 E4M3 vLLM-compatible release of `Jackrong/Qwopus3.6-35B-A3B-Coder` using the official Qwen3.6-35B-A3B FP8 quantization format. Local vLLM smoke and 30-question checks were run before upload; answer-only QA passed with no empty answers, no binary/unicode replacement garbage, and no max-token hits.
+## FP8 release validation
+This repository is the vLLM-compatible fine-grained FP8 E4M3 release of `Jackrong/Qwopus3.6-35B-A3B-Coder`.
+- Target repo: `Jackrong/Qwopus3.6-35B-A3B-Coder-FP8`
+- Source repo: `Jackrong/Qwopus3.6-35B-A3B-Coder`
+- Format: Qwen3.6 fine-grained FP8 layout with per-expert MoE tensors and `*_scale_inv` tensors.
+- Local vLLM smoke test: passed; output loaded normally and did not show binary/unicode replacement garbage.
+- 30-question vLLM test: completed 30/30; answer-only QA passed with no empty answers, no binary/unicode replacement garbage, and no max-token hits.
+- Observed benchmark throughput: 51.80 tokens/s.
+Local validation artifacts on the release machine:
+- Smoke log: `/workspace/renji-training/logs/qwopus36_35b_coder_fp8_smoke.log`
+- Benchmark report: `/workspace/renji-training/Jackrong/Qwopus3.6-35B-A3B-Coder-FP8-vllm/test_data/vllm_fp8_30q_report.md`
+- Answer-only QA report: `/workspace/renji-training/Jackrong/Qwopus3.6-35B-A3B-Coder-FP8-vllm/test_data/answer_only_quality_gate.json`