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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NanoCode012
2025-08-08 19:09:11 +07:00
committed by GitHub
parent 50f2b94d50
commit 2974670bf8
10 changed files with 127 additions and 7 deletions

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# Finetune ArceeAI's AFM with Axolotl
[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.
This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the AFM model.
## Getting started
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).
Here is an example of how to install from main for pip:
```bash
# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
git clone https://github.com/axolotl-ai-cloud/axolotl.git
cd axolotl
pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
pip3 install --no-build-isolation -e '.[flash-attn]'
```
2. Run the finetuning example:
```bash
axolotl train examples/arcee/afm-4.5b-qlora.yaml
```
This config uses about 7.8GiB VRAM.
Let us know how it goes. Happy finetuning! 🚀
### TIPS
- For inference, the official Arcee.ai team recommends `top_p: 0.95`, `temperature: 0.5`, `top_k: 50`, and `repeat_penalty: 1.1`.
- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
## Optimization Guides
- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
## Related Resources
- [AFM Blog](https://docs.arcee.ai/arcee-foundation-models/introduction-to-arcee-foundation-models)
- [Axolotl Docs](https://docs.axolotl.ai)
- [Axolotl Website](https://axolotl.ai)
- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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base_model: arcee-ai/AFM-4.5B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: false
load_in_4bit: true
datasets:
- path: fozziethebeat/alpaca_messages_2k_test
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/lora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
# save_first_step: true # uncomment this to validate checkpoint saving works with your config

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"%%capture\n",
"# This step can take ~5-10 minutes to install dependencies\n",
"!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169\""
"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8\""
]
},
{

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@@ -27,7 +27,6 @@ sequence_len: 2048
sample_packing: true
eval_sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05

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@@ -26,7 +26,6 @@ lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05

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@@ -26,7 +26,6 @@ lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05

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@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
print(
UNINSTALL_PREFIX
+ f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"'
+ 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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@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
- If you are installing from pip
```bash
pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"
pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8"
```
## Usage
@@ -31,6 +31,7 @@ plugins:
## Supported Models
- arcee
- cohere
- cohere2
- gemma
@@ -41,13 +42,17 @@ plugins:
- gemma3n_text
- glm
- glm4
- gpt_oss
- granite
- granitemoe
- hunyuan_v1_dense
- hunyuan_v1_moe
- llama
- llama4
- llama4_text
- mistral
- mistral3
- mixtral
- mllama
- phi
- phi3

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@@ -34,7 +34,7 @@ LOG = get_logger(__name__)
_CCE_INSTALL_MESSAGE = (
"Please install Axolotl's fork of cut_cross_entropy with transformers support using "
'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"`'
'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@bb8d9f8"`'
)

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@@ -37,6 +37,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
"glm4",
"smollm3",
"gpt_oss",
"arcee",
]