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?"
			}
		]
	}'

Docker Model Runner
How to use fableforge-ai/ReasonCritic-7B with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/ReasonCritic-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ReasonCritic-7B

A reasoning critic — evaluates and improves chain-of-thought.

Base model: unsloth/Qwen3-8B · Part of the FableForge ecosystem.

⚡ Quickest start — Ollama (these pulls credit the source, not a mirror)

ollama run hf.co/fableforge-ai/ReasonCritic-7B:Q4_K_M

🦙 llama.cpp

llama-cli -hf fableforge-ai/ReasonCritic-7B:Q4_K_M -p "Hello!"

💠 LM Studio

Search fableforge-ai/ReasonCritic-7B and pick a quant below.

📦 Provided quants (download one file, not the whole repo)

File	Quant	Size	Notes
reasoncritic-7b.F16.gguf	`F16`	14.43 GB	Full precision (unquantized). Reference / conversion.
reasoncritic-7b.Q8_0.gguf	`Q8_0`	8.71 GB	Extremely high quality — usually overkill.
reasoncritic-7b.Q6_K.gguf	`Q6_K`	6.73 GB	Very high quality, near-perfect. Recommended if you have the RAM.
reasoncritic-7b.Q5_K_M.gguf	`Q5_K_M`	5.85 GB	High quality. Recommended.
reasoncritic-7b.Q4_K_M.gguf	`Q4_K_M`	5.03 GB	⭐ Best size/quality balance — default pick for most users.
reasoncritic-7b.Q4_0.gguf	`Q4_0`	4.77 GB	Legacy format — prefer Q4_K_M.
reasoncritic-7b.Q3_K_M.gguf	`Q3_K_M`	4.12 GB	Lower quality; usable when RAM is tight.
reasoncritic-7b.Q2_K.gguf	`Q2_K`	3.28 GB	Very low quality — surprisingly usable.

Not sure? Take Q4_K_M. Low RAM → IQ4_XS. Max quality → Q6_K.

🧠 Prompt format (ChatML)

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

🌌 See it live — the FableForge demos

This family powers a galaxy of free, interactive HF Spaces:

⭐ Like & share — it helps people find the source instead of a mirror.

Downloads last month: 2,835

GGUF

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for fableforge-ai/ReasonCritic-7B

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

unsloth/Qwen3-8B

Finetuned

(749)

this model

Quantizations

2 models