初始化项目，由ModelHub XC社区提供模型
Model: Ba2han/out2 Source: Original Platform
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58
README.md
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README.md
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---
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library_name: transformers
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model_name: out2
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tags:
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- generated_from_trainer
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- unsloth
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- sft
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- trl
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licence: license
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---
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# Model Card for out2
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This model is a fine-tuned version of [None](https://huggingface.co/None).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="Ba2han/out2", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/batuhan409/huggingface/runs/yvxuw4pl)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.24.0
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- Transformers: 5.5.0
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- Pytorch: 2.10.0
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- Datasets: 4.3.0
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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"eos_token_id": 3,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 4608,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"model_name": "random_model",
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"model_type": "qwen3",
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"rope_type": "default"
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},
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"unsloth_version": "2026.6.9",
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"use_sliding_window": false,
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"vocab_size": 60800
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}
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generation_config.json
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"transformers_version": "5.5.0",
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"use_cache": false
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}
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last-checkpoint/config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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"head_dim": 128,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"max_position_embeddings": 8192,
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"max_window_layers": 36,
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"model_name": "random_model",
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-08,
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"rope_parameters": {
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"rope_theta": 500000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"unsloth_version": "2026.6.9",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 60800
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}
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last-checkpoint/generation_config.json
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