Qwen3-1.7B-ResearchReasoning-JSON-RL-GGUF

Qwen3-1.7B ResearchReasoning JSON RL — GGUF

GGUF quantizations of Ethosoft/Qwen3-1.7B-ResearchReasoning-JSON-RL, a Qwen3-1.7B fine-tune for evidence-grounded research reasoning, source-aware QA, claim verification, and strict JSON schema generation.

• Adapter repository: https://huggingface.co/Ethosoft/Qwen3-1.7B-ResearchReasoning-JSON-RL
• Base model: https://huggingface.co/Qwen/Qwen3-1.7B
• Source code: https://github.com/Ahmet2001/QA-research-SLM

The PEFT adapter was merged into the base model before GGUF conversion. The project calls the final training stage RL-lite: verifier-guided rejection sampling followed by supervised fine-tuning, not full online policy-gradient RL.

Files

GGUF structural validation

Each file was checked for a valid GGUF binary header, supported GGUF version, nonzero tensor count, and nonzero metadata count. The files were produced successfully with the llama.cpp converter and quantizer. This is a structural file validation, not a quant-specific inference or JSON benchmark.

Raw results are available in structural_validation.json.

llama.cpp

llama-cli \
  -m Qwen3-1.7B-ResearchReasoning-JSON-RL-Q5_K_M.gguf \
  -cnv \
  -n 768

For programmatic use, supply a strong system prompt that requires valid JSON, lists all required fields, disables markdown, and restricts evidence entries to source IDs present in the prompt.

Recommended output schema

{
  "task_type": "document_qa",
  "research_plan": ["read the question", "inspect the sources", "answer with evidence"],
  "evidence_needed": ["supporting source text"],
  "selected_sources": ["src_001"],
  "claims": [
    {
      "claim": "The answer is supported by src_001.",
      "status": "supported",
      "confidence": 0.9,
      "evidence": ["src_001"],
      "importance": "high"
    }
  ],
  "conflicts": [],
  "uncertainties": [],
  "answer": "Evidence-grounded answer"
}

Benchmark diagnostics

The full diagnostic results were produced with the PEFT adapter before GGUF conversion. They are included here for model context, but should not be treated as quant-specific measurements.

These are fixed-seed custom diagnostics using a strict JSON research prompt, not official leaderboard submissions. See benchmark_results.json and the adapter model card for full settings and base-model comparisons.

Quantization caution

This model is optimized for strict structured generation. Quantization can preserve semantic answers while still damaging commas, quotes, closing braces, field names, or source-ID arrays. Validate every response. For reliability-sensitive applications, prefer Q5_K_M or Q8_0 and use constrained decoding or application-level JSON repair.

VibeThinker inspiration and attribution

This project is conceptually inspired by the VibeThinker line of work on verifiable reasoning in compact models, but it was independently trained for research-oriented structured generation. It does not reuse VibeThinker weights, data, or code.

• Sen Xu et al., Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B, arXiv:2511.06221, 2025. https://arxiv.org/abs/2511.06221
• Sen Xu et al., VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models, arXiv:2606.16140, 2026. https://arxiv.org/abs/2606.16140

License

Apache-2.0, following the Qwen3 base model license. Review the original dataset licenses before redistributing derived training data.

Quant-specific evaluation status

The GGUF files passed structural validation after conversion and quantization. A CPU-only llama.cpp generation attempt on the TRUBA node exceeded a five-minute startup timeout for the Q8_0 file, so this release does not claim completed quant-specific runtime, JSON-adherence, or public-benchmark results. The benchmark table above belongs to the PEFT adapter before GGUF conversion. Re-run the evaluation suite in your target llama.cpp, LM Studio, or Ollama environment before reliability-sensitive deployment.