---

license: apache-2.0

base_model: empero-ai/Qwen3.8-2B-Distill

language:

• en

library_name: transformers

pipeline_tag: text-generation

tags:

• empero-ai
• qwen3.5
• qwen3.8
• distillation
• reasoning
• function-calling
• sft
• edge
• llama-cpp
• gguf

---

Qwen3.8-2B

This is a Q8 Quantization of Qwen 3.8-@B-Distilled

Original Distilled Model Creator is EmperoAI

Base model produced by Alibaba

> [!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 Qwen3.5 architecture support.

Qwen3.8-2B is a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-2B architecture — the smallest member of the family, trained on the same curriculum as its larger siblings. The student saw ~30,000 curated teacher traces from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, general reasoning, and instruction following, quality-filtered before training.

The objective: the same teacher, the same reasoning curriculum, in a model small enough for the edge. What changes across Qwen3.8-9B, Qwen3.8-4B and this model is the student's capacity — not the quality or the character of what it was taught.

Highlights

• Distilled chain-of-thought — every answer opens with a <think> block learned directly from Qwen3.8 2.4T A95B traces rather than synthetic self-generated reasoning.
• Same curriculum as the larger siblings — the same teacher and the same quality-filtered trace mix used for the 9B and 4B distills; only the student's capacity differs.
• 2B weight class — bf16 in ~4 GB; quantized builds run on phones, single-board computers, and CPU-only machines.
• Native function calling per Qwen3.5's specification — no wrapper or tool-specific fine-tune required.
• 262,144-token native context, inherited from the Qwen3.5 base.
• Full fine-tune — every parameter updated; not an adapter.

Model Overview

• Type: Causal Language Model (text path of a vision-language base)
• Base: Qwen/Qwen3.5-2B
• Number of Parameters: 2B
• Training: SFT (off-policy distillation) on ~30,000 teacher traces
• Teacher: Qwen3.8 2.4T A95B (internal distillation datasets)
• Context Length: 262,144 natively

Benchmark Results

Measured with lm-evaluation-harness, HF backend, identical settings for base and student. Both models are reasoning models and are evaluated with the CoT protocols (gsm8k_cot, mmlu_flan_cot_zeroshot); MMLU covers all 57 subjects (~1,700 questions). Flexible-extract is the primary metric; strict-match requires exact answer formatting.

| Task | Metric | Qwen3.5-2B (base) | Qwen3.8-2B | Δ |

|---|---|---:|---:|---:|

| gsm8k_cot | exact_match (flexible) | 0.330 | 0.640 | +0.310 |

| gsm8k_cot | exact_match (strict) | 0.545 | 0.640 | +0.095 |

| mmlu (CoT, 57 subjects) | acc (flexible-extract) | 0.283 | 0.548 | +0.265 |

| mmlu (CoT, 57 subjects) | acc (strict-match) | 0.004 | 0.225 | +0.221 |

Sampling for generation: temperature=0.6, top_p=0.95, top_k=20 (Qwen3.5 recommended settings).

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "empero-ai/Qwen3.8-2B"
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").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.shape[1]:], skip_special_tokens=True))

A recent transformers release with Qwen3.5 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.

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.
• Weight class, not curriculum: the reasoning form transfers from the same teacher traces the larger siblings learned from; what 2B parameters bound is capacity — factual recall and very hard multi-step problems. For harder workloads, step up to Qwen3.8-4B or Qwen3.8-9B.
• Scope: the trace mix emphasizes mathematics, reasoning, and instruction following; the 9B additionally trains on code, so use Qwen3.8-9B for code-heavy workloads. The fine-tune is text-only; vision behavior is inherited from the base and was not evaluated here.

Stay in the loop

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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 Qwen3.5-2B base. Shared for research and experimentation, as-is.

Acknowledgements

• Developed and released by Empero
• Base model: Qwen3.5-2B (Alibaba Qwen team)
• Training: TRL + Transformers
• Linear-attention kernels: flash-linear-attention, causal_conv1d
• Evaluation: lm-evaluation-harness (EleutherAI)