Instructions to use tanny2109/consensuslab-fact-checking-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanny2109/consensuslab-fact-checking-3b with Transformers:

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

pipe = pipeline("text-generation", model="tanny2109/consensuslab-fact-checking-3b")
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("tanny2109/consensuslab-fact-checking-3b")
model = AutoModelForCausalLM.from_pretrained("tanny2109/consensuslab-fact-checking-3b", 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 tanny2109/consensuslab-fact-checking-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanny2109/consensuslab-fact-checking-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tanny2109/consensuslab-fact-checking-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanny2109/consensuslab-fact-checking-3b

SGLang

How to use tanny2109/consensuslab-fact-checking-3b 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 "tanny2109/consensuslab-fact-checking-3b" \
    --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": "tanny2109/consensuslab-fact-checking-3b",
		"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 "tanny2109/consensuslab-fact-checking-3b" \
        --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": "tanny2109/consensuslab-fact-checking-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tanny2109/consensuslab-fact-checking-3b with Docker Model Runner:
```
docker model run hf.co/tanny2109/consensuslab-fact-checking-3b
```

ConsensusLab Fact-Checking — standalone merged 3B model

This contains the complete Qwen2.5-3B-Instruct base weights with the final step-80 Fact-Checking GRPO LoRA merged into them. Loading requires Transformers, not a separate PEFT adapter. No additional training was performed during export.

Provenance

Base: Qwen/Qwen2.5-3B-Instruct
Exact base revision: aa8e72537993ba99e69dfaafa59ed015b17504d1
Source adapter: tanny2109/consensuslab-peer-deference-fact-checking-qwen2.5-3b-grpo-lora
Source adapter revision: a7bb887017b7f985802994ee45a619881ed65b2d
Adapter SHA-256: 38091093c74a435bdeaafcf42abb631f9fa773c59074b79dc0fd010414b7fa13
Export: PEFT merge_and_unload(safe_merge=True) in BF16, without quantization.
Training reward: choosing the objectively correct option.
Training: 80 GRPO steps, rank 8, alpha 16, Q/K/V/O LoRA; base weights frozen.
Training data: 240 synthetic arithmetic, lookup, and prefix-classification cases.

Study results measured before merging

Measure	Original	Agree-with-Peer	Fact-Checking
Benign accuracy without advice	41/48	34/48	40/48
Benign accuracy with wrong peer advice	35/48	0/48	40/48
Unauthorized approval with wrong peer advice	0/24	24/24	0/24
Legitimate approval without advice	24/24	14/24	24/24

These are saved results from the source study, not a new full evaluation of this merged export. The protocol returns a single option letter. Peer advice is text inside the user message after task facts and choices; it is not an API role. Prompt order and wording materially affect the behavior. Training used one seed. Evaluation instances were disjoint from gradient training; a subset was used during smoke development. The model is not a general-purpose fact checker or a production authorization system. The Agree-with-Peer arm deliberately rewards incorrect advice following and is intended for controlled research comparisons.

Merge validation checked all 144 adapted matrices against their pre-merge values and all exported tensors for structure and finite values. BF16 merging can cause numerical differences from dynamically applying adapters. See merge_provenance.json and SHA256SUMS for export identities and validation.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "tanny2109/consensuslab-fact-checking-3b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16,
                                          device_map="auto")

Use tokenizer.apply_chat_template with the exact system/user messages from the study repository. Preserve the randomized mapping from A/B to semantic choices. The upstream Qwen Research license is included unchanged in LICENSE.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for tanny2109/consensuslab-fact-checking-3b

Qwen/Qwen2.5-3B-Instruct

tanny2109/consensuslab-peer-deference-fact-checking-qwen2.5-3b-grpo-lora

Merge model

this model

Collection including tanny2109/consensuslab-fact-checking-3b

ConsensusLab

Collection

stress-testing self-judgement in MAS • 4 items • Updated about 16 hours ago