Instructions to use Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16")
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("Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16")
model = AutoModelForMultimodalLM.from_pretrained("Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16", 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 Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16",
		"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/Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16

SGLang

How to use Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16 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 "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16" \
    --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": "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16",
		"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 "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16" \
        --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": "Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16",
		"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 Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16 with Docker Model Runner:
```
docker model run hf.co/Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16
```

⚠️ EXPERIMENTAL WEIGHTS — NOT A FINAL RELEASE ⚠️

THESE ARE EXPERIMENTAL RESEARCH WEIGHTS. THIS IS NOT A FINISHED OR PRODUCTION MODEL.

Every qwen-3.8-next-40b-exp-* repository is an intermediate checkpoint of an ongoing weight-surgery research line — not a release, not a final version, not a validated product.

Not validated for end-user deployment, standalone inference, or any downstream product.

No safety, alignment, or quality guarantees. Behaviour can change or regress between checkpoints.

Non-standard architecture (qwen4_exp): requires an inference stack that explicitly supports it.

Published only for reproducibility of MoE pruning, router-healing, PLE-calibration and distillation experiments.

If you are looking for a model to actually use, use the upstream Qwen release instead.

Qwen-3.8-Next-40B-Exp — 512→128 MoE Pruning (BF16)

Intermediate weights produced by a 512-to-128 MoE expert pruning surgery applied to Qwen3.8-Flash-Next-Uncensored. This is the raw pruned state — before router healing. Expect routing imbalance, expert starvation and repetition loops in open-ended generation; the repaired checkpoint is qwen-3.8-next-40b-exp-moe-healed-bf16.

Pruning method

Pruning ratio: 512 router experts compressed to 128 surviving experts per layer (75% reduction).
Selection: multi-stratum Pareto knapsack calibration over 5.7M tokens (CoT, coding, tool-calling and dialogue strata).
Denoising: LASER SVD denoising plus cosine affinity matching between merged experts.
Frozen: everything outside the expert banks.

Architecture (`qwen4_exp`)

Property	Value
Total layers	48 hybrid MoE blocks
Hidden size	2,560
Attention	Gated DeltaNet linear attention, 3 linear blocks per 1 full-attention block (`full_attention_interval: 4`)
Hyper-Connections	4 residual streams, rank-320 projections
Routed experts	128 per layer, top-10 active (`num_experts_per_tok: 10`)
Shared expert	1 dense expert (intermediate size 640)
PLE memory	Associative n-gram table, 2,000,000 base entries (`ngram_size: 3`)
Vocabulary	248,320
Native context	262,144 tokens
Precision	BFloat16

Checkpoint class: multimodal (Qwen4ExpForConditionalGeneration, 27-layer vision tower).

Repository contents

File	Notes
`model-*-of-00131.safetensors`	104 BF16 shards, ~86 GB
`model.safetensors.index.json`	Weight map (1,658 tensors, 80.83 GB)
`mascara_48_capas_optima.json`	Per-layer surviving-expert mask (48 layers)
`DONE_CIRUGIA_MOE_COMPLETA.json`	Surgery completion manifest
`calib_sota_gated_5m.parquet`	Calibration corpus used for expert selection
`config.json`, `generation_config.json`, `chat_template.jinja`, `tokenizer.json`	Configuration and tokenizer

Shard naming: files are named -of-00131, but the checkpoint is complete with 104 shards — exactly what model.safetensors.index.json references. The 00131 suffix is a leftover from the original sharding run; nothing is missing.

Known limitations

Routing is unbalanced: ~38% of experts see little or no traffic, and some layers concentrate up to 8.8% of all tokens in a single expert.
Open-ended generation can fall into lexical repetition loops.
PLE memory injection ratio is roughly double the stable range.

Experiment line

Repository	Role
`qwen-3.8-next-40b-exp-clean-bf16`	Clean baseline
`qwen-3.8-next-40b-exp-clean-healed-bf16`	Router-healed, text-only
`qwen-3.8-next-40b-exp-moe-pruned-bf16`	512→128 expert pruning
`qwen-3.8-next-40b-exp-moe-healed-bf16`	Pruned + router-healed + PLE 2.0M
`qwen-3.8-next-40b-exp-ple-2m-table`	Routing tables and calibration artifacts
`qwen-3.8-next-40b-exp-cartridge-general`	Delta cartridge testbed
`qwen-3.8-next-40b-exp-profiling-metrics`	Profiling metrics

License

Apache 2.0, inherited from the upstream Qwen base model.

Downloads last month: 672

Safetensors

Model size

43B params

Tensor type

BF16

I64

Model tree for Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16

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Davd-b01
/

qwen-3.8-next-40b-exp-moe-pruned-bf16

⚠️ EXPERIMENTAL WEIGHTS — NOT A FINAL RELEASE ⚠️

THESE ARE EXPERIMENTAL RESEARCH WEIGHTS. THIS IS NOT A FINISHED OR PRODUCTION MODEL.

Qwen-3.8-Next-40B-Exp — 512→128 MoE Pruning (BF16)

Pruning method

Architecture (`qwen4_exp`)

Repository contents

Known limitations

Experiment line

License

Model tree for Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16

⚠️ EXPERIMENTAL WEIGHTS — NOT A FINAL RELEASE ⚠️

THESE ARE EXPERIMENTAL RESEARCH WEIGHTS. THIS IS NOT A FINISHED OR PRODUCTION MODEL.

Qwen-3.8-Next-40B-Exp — 512→128 MoE Pruning (BF16)

Pruning method

Architecture (qwen4_exp)

Repository contents

Known limitations

Experiment line

License

Model tree for Davd-b01/qwen-3.8-next-40b-exp-moe-pruned-bf16

Architecture (`qwen4_exp`)