Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled is a 4-billion-parameter language model fine-tuned to excel at step-by-step reasoning tasks. It distills reasoning patterns from Claude 4.6 Opus using supervised fine-tuning and LoRA, outputting structured thinking in <think> tags before final answers. The model is optimized for GGUF format (quantized inference) and supports 16K token context. It shows modest benchmark improvements over the base Qwen3.5-4B on reasoning-heavy tasks (GPQA, ARC). Best suited for offline analytical work: math, coding, logic puzzles, and domains where reasoning transparency matters.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Jackrong |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 46.1k |
| Likes | 138 |
| Last updated | 2026-04-06 |
| Source | Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF |
What Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF is
Base: Qwen3.5-4B dense transformer. Training: Supervised Fine-Tuning (SFT) with LoRA via Unsloth, masked on assistant responses only. Dataset: ~3000 Claude 4.6 Opus reasoning examples (nohurry/Opus-4.6-Reasoning-3000x-filtered) + 700 Qwen3.5-specific reasoning samples (Jackrong/Qwen3.5-reasoning-700x) + 250 high-intensity examples (TeichAI/claude-4.5-opus). Training loss converged from 0.74 → 0.24. Output format: enforced <think>{reasoning}</think>\n{answer}. Context window: 16,384 tokens. Quantized as GGUF for local/edge inference.
Run Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated: GGUF 4-bit quantization ~1.5–2.5 GB VRAM (GPU) or CPU inference if patient with latency. Base fp16: ~8 GB VRAM. Full SFT training (fine-tuning): likely 16–24 GB VRAM with LoRA. Unsloth optimizations reduce memory vs. standard training. Exact requirements not specified; verify with your quantization tool.
LoRA-friendly. Card confirms the model itself was trained via Unsloth + LoRA (response-only masking). GitHub repo and PDF guide provided for reproducibility. Users can further fine-tune with additional reasoning data or domain-specific examples using Unsloth. No explicit QLoRA notes, but GGUF quantization suggests post-hoc inference optimization rather than training-time quantization.
When to avoid it — and what to weigh
- Real-time Fact Lookup Required — Model has no external knowledge retrieval or up-to-date facts. Card explicitly notes hallucination risk if reasoning relies on real-world events not in training data. Requires RAG or external fact sources.
- Production High-Throughput Inference — 4B is small but GGUF quantization targets local/edge serving, not cloud-scale multi-tenant APIs. Latency and throughput characteristics for production SaaS use are unknown.
- Language Tasks Beyond CoT Domains — Model is distilled for reasoning; general-purpose NLG tasks (summarization, creative writing, retrieval) may underperform. Fine-tuning was narrow (SFT on reasoning data only).
- Regulatory or Compliance-Critical Applications — Card labels this a 'test version for academic research' use only. No security audit, no compliance certification mentioned. Not recommended for healthcare, finance, or legal decision-making.
License & commercial use
Apache 2.0. This is an OSI-approved permissive license allowing commercial use, modification, and distribution, provided original license and copyright notice are retained. No copyleft or attribution restrictions beyond standard Apache 2.0 terms.
Apache 2.0 permits commercial use. However, the card explicitly states: 'This model is a test version intended solely for learning and demonstration purposes, and is for academic research and technical exploration use only.' This disclaimer may create ambiguity: the license permits commercial use, but the developer's stated intent is non-commercial research. For regulated or high-stakes commercial applications, clarify directly with the developer (Jackrong) whether production use is approved. For low-risk commercial R&D or internal tools, Apache 2.0 should suffice.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Needs review |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit or threat model provided. As an autoregressive LLM distilled from Claude data, it inherits typical LLM risks: prompt injection, hallucination, and potential unintended memorization of training data. GGUF quantization does not inherently add or remove security guarantees. Card notes hallucination risk; no mention of adversarial testing, red-teaming, or safety fine-tuning. For sensitive applications, conduct your own risk assessment.
Alternatives to consider
Mistral 7B
Larger (7B), open-source, permissive Apache 2.0 license. Better general-purpose instruction following. GGUF available. Higher resource cost but more mature ecosystem.
OpenAI o1-mini or Claude API (cloud)
If reasoning quality is the priority and local deployment is not required, cloud APIs offer proven CoT reasoning without custom fine-tuning or maintenance burden. Higher cost, no privacy/local control.
Llama 3.2 1B or 3B
Smaller, optimized for edge/mobile. Llama 3.2 is actively maintained by Meta with broader ecosystem support. Trade-off: reasoning capability is lower than this Qwen distilled model.
Ship Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with senior software developers
Download the GGUF model, review the GitHub training guide, and run reasoning tasks offline. Ideal for math, coding, and analytical workflows. No API calls, full transparency, Apache 2.0 licensed.
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Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF FAQ
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Ready to Deploy Local Reasoning AI?
Download the GGUF model, review the GitHub training guide, and run reasoning tasks offline. Ideal for math, coding, and analytical workflows. No API calls, full transparency, Apache 2.0 licensed.