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Model: psymon/mistral-7b-mio-arc-fp16
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: other
+license_name: apache-2.0-mixed-data-terms
+library_name: transformers
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-v0.1
+datasets:
+- allenai/ai2_arc
+- allenai/openbookqa
+- allenai/qasc
+- allenai/sciq
+tags:
+- mistral
+- qlora
+- preference-learning
+- mutual-information-optimization
+- mio
+- hard-negative-mining
+- arc-challenge
+- science-qa
+- multiple-choice
+- fp16
+model-index:
+- name: AIRE-ARC-Mistral-7B-SFT-MIO-FP16
+  results:
+  - task:
+      type: question-answering
+      name: ARC-Challenge 25-shot
+    dataset:
+      type: allenai/ai2_arc
+      name: AI2 ARC-Challenge
+      config: ARC-Challenge
+      split: test
+    metrics:
+    - type: acc_norm
+      name: Normalized Accuracy
+      value: 0.765358361774744
+    - type: acc
+      name: Accuracy
+      value: 0.7474402730375427
+---
+
+# AIRE-ARC-Mistral-7B-SFT-MIO-FP16
+
+A standalone FP16 fine-tune of [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) for ARC-Challenge and related science multiple-choice reasoning. The model was trained in two stages:
+
+1. completion-only QLoRA SFT on 27,294 public science-QA training examples;
+2. MIO preference fine-tuning on 47,087 train-only pairs, where the rejected answers were the incorrect choices that the SFT model found most plausible.
+
+The final model reached **76.54% `acc_norm`** and **74.74% raw accuracy** on the 1,172-example ARC-Challenge test split with 25-shot evaluation. Model selection was completed on the validation split before the test split was opened.
+
+> **Important:** this is a domain-specialized completion model, not a chat or general instruction model. It improves science and commonsense multiple-choice reasoning, but capability-retention audits found regressions on MMLU Humanities, MMLU Social Sciences, and WikiText perplexity.
+
+## Model summary
+
+| Item | Value |
+|---|---|
+| Architecture | `MistralForCausalLM` |
+| Parameters | 7B |
+| Parent model | `mistralai/Mistral-7B-v0.1` |
+| Parent revision | `27d67f1b5f57dc0953326b2601d68371d40ea8da` |
+| Repository format | Standalone merged FP16 weights |
+| Adapter required | No |
+| Primary language | English |
+| Primary task | Science multiple-choice QA |
+| Training hardware | One NVIDIA A100-SXM4 40GB |
+
+The repository contains the tokenizer and merged model weights. PEFT and bitsandbytes are not required for inference. `merge_summary.json` records the merge provenance used for the submitted checkpoint.
+
+## Intended use
+
+Suitable uses include:
+
+- evaluating science and commonsense multiple-choice reasoning;
+- studying model-scored hard-negative selection;
+- reproducing the reported ARC-Challenge evaluation;
+- research on preference optimization for short-answer QA.
+
+The model is not intended for safety-critical use, factual deployment without verification, general chat, or instruction following. No additional safety alignment was performed.
+
+## Prompt format
+
+Training and evaluation use answer-text completions rather than answer-position labels:
+
+```text
+Question: {question}
+Answer:
+```
+
+The expected completion is the answer text, including its leading space. No chat template is used.
+
+## Quick start
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "psymon/mistral-7b-mio-arc-fp16"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+if tokenizer.pad_token_id is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    dtype=torch.float16,
+    device_map="auto",
+)
+model.eval()
+
+prompt = "Question: What is a worldwide increase in temperature called?\nAnswer:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=24,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+completion = tokenizer.decode(
+    output[0, inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)
+print(completion)
+```
+
+For multiple-choice evaluation, compare the conditional log-likelihood of each answer text. Do not convert the choices to `A`, `B`, `C`, and `D` unless the evaluation protocol is changed accordingly.
+
+## Training data
+
+Only public **training splits** were used for optimization. ARC-Challenge validation was used for deduplication, failure analysis, and model selection. ARC-Challenge test was not used during training or model selection.
+
+### SFT corpus
+
+| Source | Examples after filtering | Role |
+|---|---:|---|
+| ARC-Challenge | 1,117 | SFT and preference candidates |
+| ARC-Easy | 2,241 | SFT and preference candidates |
+| OpenBookQA | 4,826 | SFT and preference candidates |
+| QASC | 7,514 | SFT and preference candidates |
+| SciQ | 11,596 | SFT only |
+| **Total** | **27,294** | |
+
+The SFT prompt contains the question only; the target is the correct answer text. Choices were retained for data normalization and duplicate checks but were not included in the SFT prompt.
+
+### Preference corpus
+
+The SFT model scored every answer choice for 15,698 train-only prompts from ARC-Challenge, ARC-Easy, OpenBookQA, and QASC. Each score was the answer-token log-likelihood sum divided by the answer's character length. For each question, up to three highest-scoring incorrect choices were paired with the correct answer:
+
+```text
+prompt   = "Question: {question}\nAnswer:"
+chosen   = " {correct answer text}"
+rejected = " {model-selected incorrect answer text}"
+```
+
+This produced 47,087 preference pairs. Seven three-choice questions contributed two pairs each; the remaining questions contributed three pairs each. In 3,081 prompts, at least one incorrect choice received a higher selection score than the correct answer.
+
+## Training procedure
+
+### Stage 1: completion-only SFT
+
+| Hyperparameter | Value |
+|---|---:|
+| Quantization | 4-bit NF4 QLoRA |
+| Compute dtype | BF16 |
+| LoRA target | all linear layers |
+| LoRA rank / alpha / dropout | 16 / 32 / 0.05 |
+| Learning rate | `1e-4` |
+| Scheduler | cosine |
+| Effective batch size | 16 |
+| Maximum sequence length | 384 |
+| Epochs / optimizer steps | 1 / 1,706 |
+
+Loss was computed only on the answer completion; prompt tokens were excluded from the labels.
+
+### Stage 2: MIO preference fine-tuning
+
+MIO (Mutual Information Optimization) contrasts a chosen and rejected completion against a frozen reference model. This run used the merged SFT checkpoint as the reference and initialized a new LoRA policy from the same weights. Completion log-probabilities were averaged over response tokens, including EOS.
+
+| Hyperparameter | Value |
+|---|---:|
+| Preference pairs | 47,087 |
+| Beta | 0.5 |
+| Learning rate | `7.5e-6` |
+| LoRA rank / alpha / dropout | 16 / 32 / 0.0 |
+| Effective batch size | 32 |
+| Maximum sequence length | 256 |
+| Epochs / optimizer steps | 1 / 1,472 |
+
+The initial policy/reference log-ratios were exactly zero and the initial loss was `1.386294`, matching `ln(4)`. Reference inference was executed first under `no_grad`; its activations were released before the policy forward pass to fit training on a 40GB A100 without changing the objective.
+
+The result should be interpreted as the effect of the **complete procedure**: SFT reference, model-scored hard negatives, mean completion log-probabilities, and MIO. This experiment did not isolate the causal contribution of each component.
+
+## Evaluation
+
+Evaluation used EleutherAI's `lm-evaluation-harness` protocol with 25 few-shot examples and answer-choice likelihood normalization.
+
+### ARC-Challenge validation
+
+| Model stage | `acc` | `acc_norm` |
+|---|---:|---:|
+| Mistral-7B-v0.1 | 51.51% | 56.52% |
+| Completion-only SFT | 57.86% | 59.20% |
+| SFT + MIO | 71.57% | **73.24%** |
+
+From SFT to MIO, 51 normalized predictions changed from incorrect to correct and 9 changed from correct to incorrect, for a net gain of 42 correct answers on 299 validation examples.
+
+### One-time held-out test
+
+| Split | Documents | Few-shot | `acc` | `acc_norm` |
+|---|---:|---:|---:|---:|
+| ARC-Challenge test | 1,172 | 25 | 74.74% | **76.54%** |
+
+The candidate model was locked before this evaluation. The test split was evaluated once, and no post-test training, hyperparameter tuning, or model reselection was performed.
+
+### Post-test overlap audit
+
+The test split was compared with the training ledger only after final evaluation:
+
+- exact Test/SFT ID overlap: 0;
+- exact question plus complete choice-set overlap: 0;
+- exact normalized question-stem overlap: 9 test rows;
+- character similarity of at least 0.90: 18 test rows.
+
+Removing the 18 near-overlap rows changed test `acc_norm` from 76.5358% to **76.4298%** (-0.1060 percentage points). This is a sensitivity analysis, not proof that semantic contamination is absent; paraphrases and shared underlying facts may remain undetected.
+
+## Capability-retention audit
+
+After model selection and the one-time ARC test, the standalone Hub checkpoint was downloaded again and compared with the base model. These results were not used for training or selection.
+
+| Benchmark | Metric | Base | Final | Change |
+|---|---|---:|---:|---:|
+| HellaSwag | `acc_norm` | 81.17% | 85.43% | +4.26 pp |
+| PIQA | `acc_norm` | 82.48% | 85.75% | +3.26 pp |
+| WinoGrande | `acc` | 75.37% | 80.03% | +4.66 pp |
+| MMLU Humanities | `acc` | 56.43% | 53.18% | -3.25 pp |
+| MMLU Social Sciences | `acc` | 73.81% | 70.46% | -3.35 pp |
+| WikiText | word perplexity | 8.0848 | 8.7194 | +7.85% (worse) |
+
+The MMLU regressions partly depend on answer-position labels: accuracy fell much more for answers at positions `B` and `D` than for `A` and `C`. This suggests that output-label calibration changed in addition to any knowledge loss. WikiText does not use answer labels and also regressed, so format calibration alone cannot explain all of the degradation.
+
+## Reproducing the ARC evaluation
+
+```bash
+pip install "lm-eval==0.4.12"
+
+python -m lm_eval \
+  --model hf \
+  --model_args "pretrained=psymon/mistral-7b-mio-arc-fp16,dtype=float16" \
+  --tasks arc_challenge \
+  --num_fewshot 25 \
+  --batch_size 8
+```
+
+The reported submission result used the official ARC-Challenge parquet files in an offline custom task, with hashes checked before evaluation. The submitted Colab notebook contains the exact data validation, model-selection lock, and one-time test procedure.
+
+## Limitations
+
+- **Domain specialization:** optimized for short English science-QA completions.
+- **Not a chat model:** no instruction-following or conversational alignment was added.
+- **Single training seed:** training variance was not estimated across seeds.
+- **Public-corpus overlap risk:** post-test string audits cannot exclude semantic overlap or paraphrases.
+- **Capability trade-offs:** MMLU and WikiText regressions show that the model did not preserve all base capabilities.
+- **Incomplete coverage:** code, mathematics, safety, long-form generation, summarization, and multilingual behavior were not evaluated.
+- **No safety guarantee:** outputs may be incorrect, biased, or unsafe.
+
+## License and data terms
+
+The parent model is released under Apache-2.0. The training sources have separate terms:
+
+| Source | Terms shown by the source dataset card |
+|---|---|
+| ARC | CC BY-SA 4.0 |
+| QASC | CC BY 4.0 |
+| SciQ | CC BY-NC 3.0 |
+| OpenBookQA | No license declared in the pinned Hugging Face dataset card |
+
+Because the SFT stage includes SciQ and the OpenBookQA card does not declare complete licensing information, this model card intentionally uses `license: other` rather than presenting the repository as unconditionally Apache-2.0. The repository is shared for research and coding-test evaluation. Users are responsible for reviewing the parent-model license and each source dataset's terms before redistribution or downstream use, especially commercial use. This section is informational and not legal advice.
+
+## References
+
+- A. Q. Jiang et al., [Mistral 7B](https://arxiv.org/abs/2310.06825), 2023.
+- T. Dettmers et al., [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314), 2023.
+- X. Lv et al., [The Hidden Link Between RLHF and Contrastive Learning](https://arxiv.org/abs/2506.22578), ICLR 2026.
+- P. Clark et al., [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457), 2018.

config.json

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+  ],
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+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
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+    "rope_type": "default"
+  },
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+  "tie_word_embeddings": false,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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+  "transformers_version": "5.13.1"
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+}

tokenizer_config.json

@@ -0,0 +1,18 @@
+{
+  "add_prefix_space": null,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": [],
+  "is_local": true,
+  "legacy": false,
+  "local_files_only": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}