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

Libraries

How to use majentik/Qwen3.6-35B-A3B-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="majentik/Qwen3.6-35B-A3B-FP8")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("majentik/Qwen3.6-35B-A3B-FP8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use majentik/Qwen3.6-35B-A3B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Qwen3.6-35B-A3B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Qwen3.6-35B-A3B-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/majentik/Qwen3.6-35B-A3B-FP8

SGLang

How to use majentik/Qwen3.6-35B-A3B-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 "majentik/Qwen3.6-35B-A3B-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Qwen3.6-35B-A3B-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "majentik/Qwen3.6-35B-A3B-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Qwen3.6-35B-A3B-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use majentik/Qwen3.6-35B-A3B-FP8 with Docker Model Runner:
```
docker model run hf.co/majentik/Qwen3.6-35B-A3B-FP8
```

Status (2026-07-07): no weights published yet. This repository currently contains only the model card — it marks a planned variant that has not been released. Follow the repo to be notified when files land.

Qwen3.6-35B-A3B-FP8

Summary

Reference wrapper around Qwen/Qwen3.6-35B-A3B-FP8 — the official FP8 release. This repository carries no weights; it exists only to anchor the FP8 variant inside the majentik/* family navigation.

Why this variant

Pick this for Hopper / Ada / Blackwell GPUs where FP8 is natively supported and you want the closest-to-bf16 fidelity with ~50% memory savings. For additional compression pick one of the 4-bit variants below.

Hardware compatibility

Device	VRAM	Recommendation
H100 / H200	80–141 GB	native
RTX 4090	24 GB	does not fit full precision — use 4-bit
RTX 5090	32 GB	native

Reproduce

# No re-quantization needed — use the upstream weights directly.
huggingface-cli download Qwen/Qwen3.6-35B-A3B-FP8

Evaluation

This FP8 pack is a weights + config mirror and is not loadable by mlx_lm, so it is not covered by the local MLX eval harness. For measured scores on this family, see the benchmark tables on the sibling MLX variants (e.g. qwen3.6-35b-a3b-mlx-mxfp4 and qwen3.6-35b-a3b-mlx-nvfp4), which were evaluated with mlx_lm.evaluate (arc_easy, hellaswag; limit 200).