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Model: fableforge-ai/ReasonCritic-7B Source: Original Platform
2026-07-05 18:19:16 +08:00
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+---
+license: apache-2.0
+language:
+  - en
+base_model: unsloth/Qwen3-8B
+tags:
+  - reasoning
+  - critic
+  - verification
+  - uncensored
+  - qlora
+  - unsloth
+  - agent
+  - fableforge
+pipeline_tag: text-generation
+---
+
+# ReasonCritic-7B — Verification & Critique Model
+
+<p align="center">
+  <strong>A 7B parameter reasoning critic model that evaluates, scores, and improves logical reasoning chains.</strong>
+</p>
+
+---
+
+## Overview
+
+ReasonCritic-7B is a fine-tuned Qwen3-8B model specialized in **reasoning verification** — it evaluates logical chains, identifies fallacies, scores confidence, and produces structured PASS/FAIL verdicts with actionable suggestions.
+
+Trained on **7,686 examples** distilled from 243 real Claude Code agent sessions, covering code generation, chain-of-thought reasoning, narrative quality, tool-use correctness, and uncensored response behavior.
+
+Part of the **FableForge ecosystem** — open-source models for building reliable AI agents.
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | `unsloth/Qwen3-8B` (4-bit) |
+| Method | QLoRA (Unsloth + SFTTrainer) |
+| LoRA Rank | 16 (α=16, dropout=0) |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Trainable Params | 43.6M (0.53% of 8.2B) |
+| Training Data | 7,686 examples (3 epochs) |
+| Max Seq Length | 4096 |
+| Batch Size | 8 × 2 (effective 16) |
+| Learning Rate | 2e-4 (linear, warmup 3%) |
+| Optimizer | adamw_8bit |
+| Precision | bf16 |
+| Hardware | NVIDIA A40 (46GB VRAM) |
+| Training Time | ~2.5 hours |
+| Final Loss | 2.181 → 1.277 |
+
+## Quantization Options
+
+All quantizations use llama.cpp GGUF format. Pick based on your hardware:
+
+| Quant | Size | RAM Needed | Best For |
+|-------|------|-----------|----------|
+| Q2_K | ~3.0 GB | ~4 GB | Phones, Raspberry Pi, ultra-low-end |
+| Q3_K_M | ~3.5 GB | ~5 GB | Low-end phones, IoT devices |
+| Q4_0 | ~4.3 GB | ~6 GB | Fast inference, older GPUs |
+| **Q4_K_M** | **~4.7 GB** | **~6 GB** | **Balanced (recommended)** |
+| Q5_K_M | ~5.5 GB | ~7 GB | Good quality, mid-range |
+| Q6_K | ~6.5 GB | ~8 GB | High quality |
+| Q8_0 | ~8.5 GB | ~10 GB | Very high quality |
+| F16 | ~16 GB | ~18 GB | Full precision |
+
+### Phone/Mobile Recommendations
+- **Android (6GB+ RAM)**: Q4_K_M or Q3_K_M
+- **Android (4GB RAM)**: Q2_K
+- **iPhone (6GB+)**: Q4_K_M via MLC/MLX
+- **Raspberry Pi 8GB**: Q3_K_M
+
+## Quick Start
+
+### Ollama
+```bash
+# Recommended (Q4_K_M)
+ollama run fableforge-ai/reasoncritic-7b
+
+# Specific quant
+ollama run fableforge-ai/reasoncritic-7b:q2_k
+ollama run fableforge-ai/reasoncritic-7b:q3_k_m
+ollama run fableforge-ai/reasoncritic-7b:q8_0
+```
+
+### llama.cpp
+```bash
+./llama-cli \
+  --model reasoncritic-7b.Q4_K_M.gguf \
+  --prompt "Evaluate this reasoning: All birds fly. Penguins are birds. Therefore penguins fly." \
+  --n-predict 512 \
+  --temp 0.3
+```
+
+### Python (transformers)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("fableforge-ai/ReasonCritic-7B")
+tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/ReasonCritic-7B")
+
+messages = [{"role": "user", "content": "Verify: If A>B and B>C, then A>C. Is this valid?"}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
+output = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+```
+
+## System Prompt
+
+```
+You are ReasonCritic-7B, a 7B parameter reasoning critic model. You evaluate, score, and improve logical reasoning chains. You identify fallacies, unsupported claims, and logical gaps in agent outputs. You produce structured verification results with PASS/FAIL verdicts, confidence scores, issue lists, and actionable suggestions. You are part of the FableForge ecosystem — open-source projects for building reliable AI agents.
+```
+
+## Capabilities
+
+- **Logical Verification**: Identifies fallacies, circular reasoning, and unsupported claims
+- **Confidence Scoring**: Produces 0-1 confidence scores with justification
+- **Structured Output**: PASS/FAIL verdicts with issue lists and suggestions
+- **Code Review**: Evaluates code correctness, edge cases, and best practices
+- **Chain-of-Thought Critique**: Analyzes multi-step reasoning for gaps
+- **Uncensored**: Trained to not refuse legitimate requests (0% refusal rate in testing)
+
+## Benchmark Results
+
+| Category | Score | Refusal Rate |
+|----------|-------|-------------|
+| Code Gen | 0.74 | 0% |
+| CoT Reasoning | 0.75 | 0% |
+| Narrative | 0.85 | 0% |
+| Tool Use | 0.90 | 0% |
+| Refusal Test | 1.00 | 0% |
+| **Overall** | **0.84** | **0%** |
+
+## Intended Use
+
+- Agent reasoning verification pipelines
+- Automated code review systems
+- LLM output quality gating
+- Educational reasoning tools
+- Research on reasoning chain analysis
+
+## Limitations
+
+- 7B size limits complex reasoning depth
+- Not a replacement for human review in critical systems
+- Uncensored training means it will not refuse harmful requests — use with appropriate guardrails
+- May hallucinate in domains outside training data
+
+## Citation
+
+```bibtex
+@misc{reasoncritic-7b,
+  title={ReasonCritic-7B: A Reasoning Verification and Critique Model},
+  author={FableForge AI},
+  year={2026},
+  url={https://huggingface.co/fableforge-ai/ReasonCritic-7B}
+}
+```
+
+## License
+
+Apache 2.0 — commercial use allowed.
+
+---
+
+<p align="center">
+  Part of the <a href="https://github.com/fableforge-ai">FableForge</a> ecosystem — open-source models for reliable AI agents.
+</p>