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Model: SlowGuess/ABForge-Qwen3-8B Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-8B
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datasets:
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- SlowGuess/abforge-data
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- ablation-study
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- scientific-reasoning
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- post-training
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- qwen3
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---
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# ABForge-Qwen3-8B
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<p align="center">
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📄 <a href="https://arxiv.org/abs/XXXX.XXXXX">ArXiv</a>
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 ｜  💻 <a href="https://github.com/SlowGuess/Abforge_1">Code</a>
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 ｜  🤗 <a href="https://huggingface.co/collections/SlowGuess/abforge-6a2ac561d0e97f11e409dd75">Collection</a>
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</p>
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## About
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This repository contains **ABForge-Qwen3-8B**, the model presented in *ABForge:
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Post-Training for Paper-Grounded Ablation Design*. Given a paper's methodology with its
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ablation content removed, it proposes the ablation objectives the paper should investigate
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and designs a rigorous experiment plan for each — both from a single checkpoint.
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The model is obtained through an **SFT → GRPO** pipeline on
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[Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B), trained throughout on a 1:1 mixture of the
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two tasks:
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1. Supervised fine-tuning on the ABForge SFT corpus, one full epoch →
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[ABForge-Qwen3-8B-SFT](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-SFT)
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2. Rubric-guided GRPO, 200 updates, each rollout routed to its task's reward by
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`data_source` → **this model**
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## Links
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| Resource | Link |
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|---|---|
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| Code | [SlowGuess/Abforge_1](https://github.com/SlowGuess/Abforge_1) |
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| Training & evaluation data | [SlowGuess/abforge-data](https://huggingface.co/datasets/SlowGuess/abforge-data) |
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| SFT checkpoint | [SlowGuess/ABForge-Qwen3-8B-SFT](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-SFT) |
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| RL-only checkpoint | [SlowGuess/ABForge-Qwen3-8B-RL](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-RL) |
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| Per-paper outputs & judge rationales | `outputs/task{1,2}/*/abforge.jsonl` in the data repo |
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## Training data
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[SlowGuess/abforge-data](https://huggingface.co/datasets/SlowGuess/abforge-data) ships a
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single table under `train/`, one row per paper, built by a semi-automated audit-in-the-loop
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pipeline over research papers from major ML, NLP and CV venues. Both stages select their rows
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from that table by its split columns and mix the two tasks 1:1:
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- **SFT** — `in_sft_task1` (45,961 papers) + `in_sft_task2` (36,955)
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- **GRPO** — `in_rl_task1` + `in_rl_task2` (30,000 papers each, disjoint from the SFT pool)
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The benchmark papers carry no training flag, so they cannot leak into either stage. The
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preprocessing scripts in the code release turn the table into the training parquets directly.
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## Performance
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AblationBench, automated rubric-based LLM-as-a-Judge evaluation (`eval/ablationbench_200.jsonl`,
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200 papers, judge `claude-sonnet-4-6`). Task 1 is ablation objective identification
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(`paper_score`); Task 2 is ablation plan synthesis (`design_score`, ×100).
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| Model | Task 1 | Task 2 |
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|---|---|---|
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| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (base) | 44.4 | 43.4 |
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| [ABForge-Qwen3-8B-SFT](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-SFT) (SFT only) | 30.7 | 52.2 |
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| [ABForge-Qwen3-8B-RL](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-RL) (RL only) | 52.2 | 54.9 |
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| **ABForge-Qwen3-8B** (SFT → GRPO) | **55.9** | **62.4** |
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> Each stage on its own falls short: SFT trades Task 1 for Task 2, and GRPO from the base model
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> plateaus below the full pipeline. Running GRPO from the SFT checkpoint is worth +3.7 on Task 1
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> and +7.5 on Task 2 over RL alone.
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## Evaluation
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Reproduce the numbers above with the [code release](https://github.com/SlowGuess/Abforge_1):
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```bash
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git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1
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huggingface-cli download SlowGuess/abforge-data --repo-type dataset \
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--include "eval/*" --local-dir data
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python run_inference_local.py --task 1 \
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--input data/eval/ablationbench_200.jsonl \
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--output outputs/task1_infer.jsonl \
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--model-path SlowGuess/ABForge-Qwen3-8B \
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--dtype bf16 --device-map auto \
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--max-new-tokens 5120 --temperature 0.0 --stop-on '</Result>'
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export JUDGE_API_BASE=https://api.openai.com/v1
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export JUDGE_API_KEY=...
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export JUDGE_MODEL=...
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scripts/evaluate_task1.sh outputs/task1_infer.jsonl
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```
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Swap `--task 2`, `--stop-on '</Proposed_Plan>'` and `scripts/evaluate_task2.sh` for Task 2. The
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model is trained on the prompt templates in the code release and the rubric evaluator expects
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the matching output structure, so use those templates and greedy decoding.
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## Citation
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<!-- TODO: replace with the final paper citation once available. -->
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```bibtex
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@misc{abforge2026,
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title={ABForge: Post-Training for Paper-Grounded Ablation Design},
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author={TODO},
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year={2026},
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}
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```
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.52.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
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Normal file
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239
tokenizer_config.json
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239
tokenizer_config.json
Normal file
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"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user