Feat: add seedoss (#3104) [skip ci]
* feat: add seedoss cce * feat: add seedoss config and docs * fix: shouldn't have target modules with target linear * feat: add vram numbers * fix: hf link * fix: name * fix: support multipack seedoss * fix: merge error * feat: update seedoss instructions for transformers release
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examples/seed-oss/README.md
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examples/seed-oss/README.md
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# Finetune ByteDance's Seed-OSS with Axolotl
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[Seed-OSS](https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd) are a series of 36B parameter open source models trained by ByteDance's Seed Team.
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This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Seed-OSS is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
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Here is an example of how to install from main for pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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git clone https://github.com/axolotl-ai-cloud/axolotl.git
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cd axolotl
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pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
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pip3 install --no-build-isolation -e '.[flash-attn]'
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# Install Cut Cross Entropy
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python scripts/cutcrossentropy_install.py | sh
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```
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2. Run the finetuning example:
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```bash
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axolotl train examples/seed-oss/seed-oss-36b-qlora.yaml
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```
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This config uses about 27.7 GiB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### TIPS
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- For inference, the official Seed Team recommends `top_p=0.95` and `temperature=1.1`.
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- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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## Optimization Guides
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
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## Related Resources
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- [ByteDance Seed Website](https://seed.bytedance.com/)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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examples/seed-oss/seed-oss-36b-qlora.yaml
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examples/seed-oss/seed-oss-36b-qlora.yaml
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base_model: ByteDance-Seed/Seed-OSS-36B-Instruct
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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load_in_8bit: false
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load_in_4bit: true
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datasets:
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- path: fozziethebeat/alpaca_messages_2k_test
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type: chat_template
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.1
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output_dir: ./outputs/lora-out
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adapter: qlora
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lora_model_dir:
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sequence_len: 2048
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sample_packing: true
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_linear: true
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 1
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saves_per_epoch: 1
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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- arcee
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- arcee
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- cohere
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- cohere
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- cohere2
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- cohere2
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- deepseek_v3
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- gemma
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- gemma
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- gemma2
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- gemma2
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- gemma3
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- gemma3
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- gemma3n_text
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- gemma3n_text
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- glm
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- glm
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- glm4
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- glm4
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- glm4_moe
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- gpt_oss
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- gpt_oss
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- granite
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- granite
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- granitemoe
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- granitemoe
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- qwen3
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- qwen3
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- qwen3_moe
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- qwen3_moe
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- smollm3
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- smollm3
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- seed_oss
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- voxtral
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- voxtral
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## Citation
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## Citation
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"smollm3",
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"smollm3",
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"gpt_oss",
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"gpt_oss",
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"arcee",
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"arcee",
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"seed_oss",
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]
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]
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