Instructions to use rovdetection/code-1b-aligned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rovdetection/code-1b-aligned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rovdetection/code-1b-aligned", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files- README.md +67 -0
- adapter_config.json +43 -0
- adapter_model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -0
- training_args.bin +3 -0
README.md
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---
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base_model: rovdetection/code-1b-pretrain
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library_name: transformers
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model_name: code-1b-aligned
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tags:
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- generated_from_trainer
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- orpo
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- trl
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licence: license
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---
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# Model Card for code-1b-aligned
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This model is a fine-tuned version of [rovdetection/code-1b-pretrain](https://huggingface.co/rovdetection/code-1b-pretrain).
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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="rovdetection/code-1b-aligned", 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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This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
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### Framework versions
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- TRL: 1.4.0
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- Transformers: 5.8.1
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.5
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- Tokenizers: 0.22.2
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## Citations
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Cite ORPO as:
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```bibtex
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@article{hong2024orpo,
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title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
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author = {Jiwoo Hong and Noah Lee and James Thorne},
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year = 2024,
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eprint = {arXiv:2403.07691}
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}
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```
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "rovdetection/code-1b-pretrain",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj",
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"k_proj",
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"o_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:63b83a3b0d1966dbeaa680c7b48ea3084dcfdd7723cd03e2fa8c3a82a2000282
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size 37768048
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:54efb2864949d275b143e3bc13e9d21c600c8a3309a7f6da6e99d0be9aa7dd0f
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size 5521
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