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.