Feat: add arcee (#3028)
* feat: add arcee * feat: add latest models supported by cce * feat: add arcee example config * chore: lint * fix: typo * feat: change to instruct * feat: add vram usage * Update README.md
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examples/arcee/README.md
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examples/arcee/README.md
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# Finetune ArceeAI's AFM with Axolotl
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[Arcee Foundation Models (AFM)](https://huggingface.co/collections/arcee-ai/afm-45b-68823397c351603014963473) are a family of 4.5B parameter open weight models trained by Arcee.ai.
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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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Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the AFM model.
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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 AFM 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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```
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2. Run the finetuning example:
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```bash
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axolotl train examples/arcee/afm-4.5b-qlora.yaml
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```
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This config uses about 7.8GiB 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 Arcee.ai team recommends `top_p: 0.95`, `temperature: 0.5`, `top_k: 50`, and `repeat_penalty: 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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- [AFM Blog](https://docs.arcee.ai/arcee-foundation-models/introduction-to-arcee-foundation-models)
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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/arcee/afm-4.5b-qlora.yaml
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examples/arcee/afm-4.5b-qlora.yaml
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base_model: arcee-ai/AFM-4.5B
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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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lora_target_modules:
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- gate_proj
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- down_proj
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- up_proj
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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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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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@@ -40,7 +40,7 @@
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"%%capture\n",
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"# This step can take ~5-10 minutes to install dependencies\n",
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"!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
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"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169\""
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"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8\""
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]
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},
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{
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sample_packing: true
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eval_sample_packing: false
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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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@@ -26,7 +26,6 @@ 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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@@ -26,7 +26,6 @@ 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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@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
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print(
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UNINSTALL_PREFIX
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+ f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"'
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+ f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8"'
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)
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@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
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- If you are installing from pip
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```bash
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pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"
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pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8"
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```
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## Usage
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@@ -31,6 +31,7 @@ plugins:
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## Supported Models
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- arcee
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- cohere
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- cohere2
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- gemma
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@@ -41,13 +42,17 @@ plugins:
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- gemma3n_text
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- glm
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- glm4
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- gpt_oss
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- granite
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- granitemoe
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- hunyuan_v1_dense
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- hunyuan_v1_moe
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- llama
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- llama4
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- llama4_text
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- mistral
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- mistral3
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- mixtral
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- mllama
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- phi
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- phi3
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@@ -34,7 +34,7 @@ LOG = get_logger(__name__)
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_CCE_INSTALL_MESSAGE = (
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"Please install Axolotl's fork of cut_cross_entropy with transformers support using "
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'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"`'
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'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8"`'
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)
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@@ -37,6 +37,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
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"glm4",
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"smollm3",
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"gpt_oss",
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"arcee",
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]
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