ReasonCritic-7B/rc7b_model_card.md

---
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>