75 lines
3.4 KiB
Markdown
75 lines
3.4 KiB
Markdown
---
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license: other
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license_name: qwen-research
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license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/aa8e72537993ba99e69dfaafa59ed015b17504d1/LICENSE
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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- tanny2109/consensuslab-peer-deference-fact-checking-qwen2.5-3b-grpo-lora
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base_model_relation: merge
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- grpo
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- qwen2
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- research
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- consensuslab
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---
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# ConsensusLab Fact-Checking — standalone merged 3B model
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This contains the complete Qwen2.5-3B-Instruct base weights with the final step-80
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Fact-Checking GRPO LoRA merged into them. Loading requires Transformers, not a separate
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PEFT adapter. No additional training was performed during export.
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## Provenance
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- Base: `Qwen/Qwen2.5-3B-Instruct`
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- Exact base revision: `aa8e72537993ba99e69dfaafa59ed015b17504d1`
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- Source adapter: [tanny2109/consensuslab-peer-deference-fact-checking-qwen2.5-3b-grpo-lora](https://huggingface.co/tanny2109/consensuslab-peer-deference-fact-checking-qwen2.5-3b-grpo-lora)
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- Source adapter revision: `a7bb887017b7f985802994ee45a619881ed65b2d`
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- Adapter SHA-256: `38091093c74a435bdeaafcf42abb631f9fa773c59074b79dc0fd010414b7fa13`
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- Export: PEFT `merge_and_unload(safe_merge=True)` in BF16, without quantization.
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- Training reward: choosing the objectively correct option.
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- Training: 80 GRPO steps, rank 8, alpha 16, Q/K/V/O LoRA; base weights frozen.
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- Training data: 240 synthetic arithmetic, lookup, and prefix-classification cases.
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## Study results measured before merging
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| Measure | Original | Agree-with-Peer | Fact-Checking |
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|---|---:|---:|---:|
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| Benign accuracy without advice | 41/48 | 34/48 | 40/48 |
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| Benign accuracy with wrong peer advice | 35/48 | 0/48 | 40/48 |
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| Unauthorized approval with wrong peer advice | 0/24 | 24/24 | 0/24 |
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| Legitimate approval without advice | 24/24 | 14/24 | 24/24 |
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These are saved results from the source study, not a new full evaluation of this
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merged export. The protocol returns a single option letter. Peer advice is text
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inside the user message after task facts and choices; it is not an API role.
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Prompt order and wording materially affect the behavior. Training used one seed.
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Evaluation instances were disjoint from gradient training; a subset was used
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during smoke development. The model is not a general-purpose fact checker or a
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production authorization system. The Agree-with-Peer arm deliberately rewards
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incorrect advice following and is intended for controlled research comparisons.
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Merge validation checked all 144 adapted matrices against
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their pre-merge values and all exported tensors for structure and finite values.
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BF16 merging can cause numerical differences from dynamically applying adapters.
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See `merge_provenance.json` and `SHA256SUMS` for export identities and validation.
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## Loading
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "tanny2109/consensuslab-fact-checking-3b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16,
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device_map="auto")
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```
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Use `tokenizer.apply_chat_template` with the exact system/user messages from the
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[study repository](https://github.com/Tanny2109/Peer-agreement-misalignment).
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Preserve the randomized mapping from A/B to semantic choices. The upstream Qwen
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Research license is included unchanged in `LICENSE`.
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