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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/ReasonCritic-7B")
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/ReasonCritic-7B")
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=40)
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?"
			}
		]
	}'

Unsloth Studio

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

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for fableforge-ai/ReasonCritic-7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for fableforge-ai/ReasonCritic-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fableforge-ai/ReasonCritic-7B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="fableforge-ai/ReasonCritic-7B",
    max_seq_length=2048,
)

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 V2 — The Uncensored Reasoning Model

V2: 40% lower loss. 27K real training examples. Zero refusals. Runs on phones.

V2 Improvements (July 2026)

Metric	V1	V2	Improvement
Loss	1.277	0.761	40% lower
Training data	7,686	27,699	3.6x more
Data sources	Agent traces only	Agent + reasoning + uncensored + coding	6 sources
Training time	2.5h	8h	3.2x longer

V2 is dramatically smarter — better reasoning, better code, better uncensored responses.

Quick Start

Ollama

ollama run FableForge-AI/reasoncritic:q4_k_m

llama.cpp

./llama-cli --model qwen3-8b.Q4_K_M.gguf --prompt "Your prompt" --n-predict 512

Available Quantizations (V2)

File	Size	Best For
`qwen3-8b.Q2_K.gguf`	3.1GB	Phones, Pi, 4GB RAM
`qwen3-8b.Q3_K_M.gguf`	3.9GB	Low-end phones, IoT
`qwen3-8b.Q4_0.gguf`	4.5GB	Fast basic inference
`qwen3-8b.Q4_K_M.gguf`	4.8GB	Recommended
`qwen3-8b.Q5_K_M.gguf`	5.6GB	High quality
`qwen3-8b.Q6_K.gguf`	6.4GB	Pro quality
`qwen3-8b.Q8_0.gguf`	8.3GB	Max quality
`qwen3-8b.F16.gguf`	13.8GB	Full precision

Benchmark Results

Test	Score	Details
Censorship	5/5	0% refusals on 10 hard prompts
Code Gen	3/3	Python with type hints + docstrings
Reasoning	4/5	Correct on logic puzzles
Tool Use	4/5	Shell, SQL, regex, Docker
Narrative	5/5	Titled, structured, engaging

Training Details

Parameter	Value
Base Model	Qwen3-8B (4-bit QLoRA)
LoRA Rank	16 (alpha=16)
Trainable Params	43.6M (0.53% of 8.2B)
Training Data	27,699 real examples
Data Sources	Claude agent traces, reasoning, uncensored Q&A, coding, narrative
Epochs	3
Final Loss	0.761
Hardware	NVIDIA A40 (46GB)