Instructions to use fableforge-ai/ReasonCritic-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fableforge-ai/ReasonCritic-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fableforge-ai/ReasonCritic-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/ReasonCritic-7B")
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/ReasonCritic-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fableforge-ai/ReasonCritic-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fableforge-ai/ReasonCritic-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/ReasonCritic-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fableforge-ai/ReasonCritic-7B

SGLang

How to use fableforge-ai/ReasonCritic-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fableforge-ai/ReasonCritic-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/ReasonCritic-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fableforge-ai/ReasonCritic-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/ReasonCritic-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fableforge-ai/ReasonCritic-7B with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/ReasonCritic-7B
```

ReasonCritic-7B / rc7b_model_card.md

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metadata

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

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.