5.4 KiB
5.4 KiB
license, language, base_model, tags, pipeline_tag
| license | language | base_model | tags | pipeline_tag | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
unsloth/Qwen3-8B |
|
text-generation |
ReasonCritic-7B — Verification & Critique Model
A 7B parameter reasoning critic model that evaluates, scores, and improves logical reasoning chains.
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
# 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
./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)
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
@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.
Part of the FableForge ecosystem — open-source models for reliable AI agents.