commit fa38774a1e050bf2c1ce0f1b7561e61884eb9b55 Author: ModelHub XC Date: Sun Sep 20 14:58:13 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: OpenDataArena/Qwen2.5-7B-ODA-Mixture-500k Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..e475b8c --- /dev/null +++ b/.gitattributes @@ -0,0 +1,64 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text + + +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +checkpoint-6870/training_args.bin filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/vocab.json filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/merges.txt filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +merges.txt filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +training_args.bin filter=lfs diff=lfs merge=lfs -text +model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +vocab.json filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-6870/model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..eab3267 --- /dev/null +++ b/README.md @@ -0,0 +1,249 @@ +--- +base_model: Qwen/Qwen2.5-7B-Base +library_name: transformers +pipeline_tag: text-generation +datasets: +- OpenDataArena/ODA-Mixture-500k +tags: +- qwen2.5 +- sft +- opendataarena +- oda-mixture-500k +license: apache-2.0 +language: +- en +metrics: +- accuracy +--- + +# Qwen2.5-7B-ODA-Mixture-500k +Leaderboard Performance + +Qwen2.5-7B-ODA-Mixture-500k is a supervised fine-tuned (SFT) model built on top of **Qwen2.5-7B-Base**, trained with **[ODA-Mixture-500k](https://huggingface.co/datasets/OpenDataArena/ODA-Mixture-500k)**. This training set is curated by mixing top-performing open corpora selected via the *[OpenDataArena](https://opendataarena.github.io)* leaderboard, and refined through deduplication and benchmark decontamination, aiming to improve the model’s general capabilities across **General**, **Math**, **Code**, and **Reasoning** domains. + +--- + +## 🧠 Model Summary + +- **Base Model**: `Qwen/Qwen2.5-7B-Base` +- **Training Data**: `OpenDataArena/ODA-Mixture-500k` +- **Domain Coverage**: General, Math, Code, Reasoning +- **Scale (selected training set)**: ~**500K** samples +- **Goal**: Improve multi-domain reasoning and problem-solving ability while achieving stable overall performance gains. + +--- + +## ⚙️ Training Data Curation Pipeline + +ODA-Mixture-500k is built by following a single rule: **trust the OpenDataArena leaderboard**. + +### 1️⃣ Data Collection + +We utilize **LIMO** as the foundational anchor due to its exceptional sample efficiency on the ODA overall leaderboard. To scale up to 500K, we integrate the top-performing and efficient corpora from specific ODA domain leaderboards, including: +- **AM-Thinking-v1-Distilled-Math** (Math domain) +- **AM-Thinking-v1-Distilled-code** (Code domain) +- **math-gpt-4o-200k** (General domain) +- **SYNTHETIC-2-SFT-verified** (Reasoning domain) + +### 2️⃣ Deduplication & Decontamination + +We first perform **exact deduplication** over all questions to remove identical items, and then run **benchmark decontamination** to reduce evaluation leakage by removing overlaps with standard and competition benchmarks. + +### 3️⃣ Data Selection + +At the 500K scale, our priority is distributional coverage. We employ semantic clustering to partition the total data pool into distinct thematic clusters. Within each cluster, we perform uniform sampling to ensure the final mixture represents a broad and balanced spectrum of reasoning tasks, maximizing the model's generalization capabilities. + +--- + +## 📚 Training Data Source Composition + +| Source | Count | Percentage | +|---|---:|---:| +| LIMO | 817 | 0.16% | +| AM-Thinking-Distilled-math | 150,244 | 29.67% | +| AM-Thinking-Distilled-code| 150,252 | 29.67% | +| math-gpt-4o-200k| 100,138 | 19.78% | +| SYNTHETIC-2-SFT-verified| 104,913 | 20.72% | + +--- + +## 🧩 Data Format + +The training data sample format is as follows (aligned with the dataset schema): + +```json +{ + "id": "unique_identifier", + "source": "data source", + "question": "textual question or instruction", + "response": "textual response" +} +``` + +--- + +## 📈 Performance + +Qwen2.5-7B-ODA-Mixture-500k is evaluated as an SFT model built on **Qwen2.5-7B-Base** across the full ODA benchmark suite spanning four domains: +- **General (DROP, IFEVAL, AGIEVAL, MMLU-Pro)** +- **Math (GSM8K, MATH500, Omni-Math, OlympiadBench, AIME2024)** +- **Code (HumanEval, MBPP, LCB (V5), HumanEval+)** +- **Reasoning (ARC-C, BBH, CALM, KOR-BENCH)**. + +We observe consistent improvements over the base checkpoint, with particularly strong gains on several benchmarks. + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Leaderboard Performance Comparison. Best scores in bold, second-best underlined. Eff. denotes Data Efficiency. +
Model / Training DataSizeEff.GeneralMathCodeReasoningAVG
Qwen2.5-7B-Base
Qwen2.5-7B-Base--51.439.850.142.746.0
OpenThoughts3-1.2M1.2M+0.01145.571.867.054.359.6
OmniThought-0528365k+0.02747.171.247.657.255.8
SYNTHETIC-2-SFT-verified105k+0.08651.369.840.158.955.0
AM-Thinking-v1-Distilled-math558k+0.01657.777.439.544.854.8
LIMO817+9.92060.744.057.953.854.1
MiroMind-M1-SFT-719K719k+0.00652.071.026.351.550.2
AM-Thinking-v1-Distilled-code324k+0.02449.952.368.744.453.8
Light-R1-SFTData79k+0.08455.564.438.851.952.7
ODA-Mixture-500k500k+0.03963.472.866.759.665.6
ODA-Mixture-101k101k+0.14956.871.264.451.561.0
+
+ +--- + +## 🌐 About OpenDataArena + +[OpenDataArena](https://opendataarena.github.io/) is an open research platform dedicated to **discovering, evaluating, and advancing high-quality datasets for AI post-training**. It provides a transparent, data-centric ecosystem to support reproducible dataset evaluation and sharing. + +**Key Features:** +- 🏆 **Dataset Leaderboard** — helps researchers identify **the most valuable and high-quality datasets across different domains** +- 📊 **Detailed Evaluation Scores** — provides **comprehensive metrics** to assess data quality, complexity, difficulty, etc. +- 🧰 **Data Processing Toolkit** — [OpenDataArena-Tool](https://github.com/OpenDataArena/OpenDataArena-Tool) offers an open-source pipeline for dataset curation and scoring. + +--- + +## 🚀 Usage + +Model repo: `OpenDataArena/Qwen2.5-7B-ODA-Mixture-500k`. Below is a minimal runnable example for loading and inference: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +MODEL_ID = "OpenDataArena/Qwen2.5-7B-ODA-Mixture-500k" + +tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto", trust_remote_code=True) + +messages = [ + {"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"}, +] +text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) +inputs = tokenizer([text], return_tensors="pt").to(model.device) + +outputs = model.generate( + **inputs, + max_new_tokens=256, + do_sample=True, + temperature=0.7, + top_p=0.9, +) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +--- + +## 📚 Citation + +If you use this model or its training data (ODA-Mixture-500k), please cite: + +```bibtex +@article{gao2025closing, + title={Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets}, + author={Gao, Xin and Wang, Xiaoyang and Zhu, Yun and Cai, Mengzhang and He, Conghui and Wu, Lijun}, + journal={arXiv preprint arXiv:2601.09733}, + year={2025} +} +``` +```bibtex +@article{cai2025opendataarena, + title={OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value}, + author={Cai, Mengzhang and Gao, Xin and Li, Yu and Lin, Honglin and Liu, Zheng and Pan, Zhuoshi and Pei, Qizhi and Shang, Xiaoran and Sun, Mengyuan and Tang, Zinan and others}, + journal={arXiv preprint arXiv:2512.14051}, + year={2025} +} +``` \ No newline at end of file diff --git a/README.md.backup b/README.md.backup new file mode 100644 index 0000000..0cdff66 --- /dev/null +++ b/README.md.backup @@ -0,0 +1,60 @@ +--- +library_name: transformers +license: other +base_model: /mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: seed-42 + results: [] +--- + + + +# seed-42 + +This model is a fine-tuned version of [/mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B](https://huggingface.co//mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B) on the overall-v4c-500k dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 42 +- distributed_type: multi-GPU +- num_devices: 32 +- total_train_batch_size: 32 +- total_eval_batch_size: 256 +- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_ratio: 0.1 +- num_epochs: 3.0 + +### Training results + + + +### Framework versions + +- Transformers 4.55.0 +- Pytorch 2.6.0+cu124 +- Datasets 3.2.0 +- Tokenizers 0.21.0 diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..482ced4 --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,24 @@ +{ + "": 151658, + "": 151657, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..5a41134 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 3.0, + "total_flos": 1.440651216420864e+16, + "train_loss": 0.30719185047274594, + "train_runtime": 200812.4774, + "train_samples_per_second": 1.094, + "train_steps_per_second": 0.034 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..d153f82 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,4 @@ +{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ 'System: ' + system_message + '<|endoftext|>' + ' +' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '<|endoftext|>' + ' +Assistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + ' +' }}{% endif %}{% endfor %} \ No newline at end of file diff --git a/checkpoint-6870/added_tokens.json b/checkpoint-6870/added_tokens.json new file mode 100644 index 0000000..482ced4 --- /dev/null +++ b/checkpoint-6870/added_tokens.json @@ -0,0 +1,24 @@ +{ + "": 151658, + "": 151657, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/checkpoint-6870/chat_template.jinja b/checkpoint-6870/chat_template.jinja new file mode 100644 index 0000000..d153f82 --- /dev/null +++ b/checkpoint-6870/chat_template.jinja @@ -0,0 +1,4 @@ +{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ 'System: ' + system_message + '<|endoftext|>' + ' +' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '<|endoftext|>' + ' +Assistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + ' +' }}{% endif %}{% endfor %} \ No newline at end of file diff --git a/checkpoint-6870/config.json b/checkpoint-6870/config.json new file mode 100644 index 0000000..fa2559a --- /dev/null +++ b/checkpoint-6870/config.json @@ -0,0 +1,59 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_act": "silu", + "hidden_size": 3584, + "initializer_range": 0.02, + "intermediate_size": 18944, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + 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