Qwen3.8-35B-A3B

Developed by Empero

[!Note] This repository contains model weights and configuration files in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.6 architecture support.

Qwen3.8-35B-A3B is a distillation of the Qwen3.8 frontier models into the Qwen3.6-35B-A3B Mixture-of-Experts architecture. The student was trained on curated teacher traces from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, code, general reasoning, instruction following, and tool use, quality-filtered before training.

The objective: bring the reasoning behavior of frontier-scale teachers into a sparse 35B that activates only 3B parameters per token and deploys on a single GPU.

Highlights

• Distilled chain-of-thought — every answer opens with a <think> block learned directly from Qwen3.8 teacher traces rather than synthetic self-generated reasoning.
• Mathematics and code emphasis — the trace mix is deliberately weighted toward hard math and competitive programming, the domains where distillation moves the needle most at this scale.
• Sparse MoE efficiency — 35B total parameters, ~3B active per token; 256 experts with 8 routed per token.
• Attention and experts both adapted — our internal MoE training pipeline updates the attention path and the routed and shared expert stacks, not just attention.
• Native function calling per Qwen3.6's specification — no wrapper or tool-specific fine-tune required.
• 262,144-token native context, inherited from the Qwen3.6 base.

Model Overview

• Type: Causal Language Model (text path of a vision-language base)
• Base: Qwen/Qwen3.6-35B-A3B
• Number of Parameters: 35B total / ~3B active per token
• Architecture: 40 layers, 256 experts, 8 experts per token, hybrid linear + full attention
• Training: SFT (off-policy distillation) on curated teacher traces via our internal MoE training pipeline
• Teachers: Qwen3.8 2.4T A95B and Qwen3.8 Flash Next (internal distillation datasets)
• Context Length: 262,144 natively

Benchmark Results

Measured with lm-evaluation-harness, HF backend, bfloat16, identical settings and seed for base and student. Zero-shot, loglikelihood scoring.

The MMLU difference is within noise (standard error 0.003 on each measurement). The ARC gains are outside it.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "empero-ai/Qwen3.8-35B-A3B-Distilled"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "A snail is at the bottom of a 10-meter well. Each day it climbs 3 meters, each night it slips back 2. How many days until it escapes?"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_tensors="pt", return_dict=True).to(model.device)

out = model.generate(**inputs, max_new_tokens=16384,
                     temperature=0.6, top_p=0.95, top_k=20, do_sample=True)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

A recent transformers release with Qwen3.6 support is required, along with the Gated DeltaNet kernels (flash-linear-attention and a CUDA-matched causal_conv1d build) — without them the linear-attention layers fall back to slow, memory-hungry PyTorch ops.

AutoModelForCausalLM loads the text path (34.7B parameters). The vision tower is retained in the checkpoint and is reachable via AutoModelForImageTextToText.

Best Practices

• Sampling: temperature=0.6, top_p=0.95, top_k=20. Greedy decoding on long generations is a known repetition-loop failure mode for reasoning models in this class.
• Output length: allow generous max_new_tokens (16,384 recommended); every answer opens with a <think> block. Parse and strip the <think>...</think> span for end users.
• Scope: the model learned from teacher traces, not from its own rollouts — it inherits the teachers' reasoning style, including occasional over-long deliberation on easy questions.

Limitations

• Shorter responses. The student was trained on 8,192-token examples and produces noticeably shorter outputs than the base. Long chains of thought are more likely to be cut short, so behaviour on long-form generation and long-context workloads may be degraded relative to the base.
• A v2 is in training with longer-context support, aimed squarely at the point above.
• Vision is untouched. The fine-tune is text-only; vision behaviour is inherited from the base and was not evaluated.

Stay in the loop

Sign up for the Empero newsletter at empero.org for releases, evals, and research notes.

Support / Donate

If this model helped you, consider supporting the project:

• BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
• LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

---

Provenance & licensing

Weights are released under Apache-2.0, inherited from the Qwen3.6-35B-A3B base. Shared for research and experimentation, as-is.

Acknowledgements

• Developed and released by Empero
• Base model: Qwen3.6-35B-A3B (Alibaba Qwen team)
• Teachers: Qwen3.8 2.4T A95B and Qwen3.8 Flash Next
• Training: TRL + Transformers
• Linear-attention kernels: flash-linear-attention, causal_conv1d
• Evaluation: lm-evaluation-harness (EleutherAI)
• Thanks to everyone who used our free community endpoint — a portion of the prompts used to generate this model's synthetic training data came from those interactions.