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3 Commits
fix/issue-
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feat/glmfl
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87e0fd6b52 | ||
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2d44432e6c | ||
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57377814e9 |
@@ -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@f4b5712\""
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"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d\""
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]
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},
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{
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40
examples/glm4.7-flash/README.md
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40
examples/glm4.7-flash/README.md
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@@ -0,0 +1,40 @@
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# Finetune Z.ai's GLM-4.7-Flash with Axolotl
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[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model.
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This guide shows how to fine-tune it with Axolotl.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
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3. Run the finetuning example:
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```bash
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axolotl train examples/glm4.7-flash/glm4.7-flash-qlora.yaml
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```
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This config uses about X 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 Z.ai team recommends `top_p: 0.95`, `temperature: 1.0`, and `max_new_tokens: 131072`.
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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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## Optimization Guides
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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Related Resources
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- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
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- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
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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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63
examples/glm4.7-flash/glm4.7-flash-qlora.yaml
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63
examples/glm4.7-flash/glm4.7-flash-qlora.yaml
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base_model: zai-org/GLM-4.7-Flash
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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_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: glm-4.7-flash
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wandb_entity:
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wandb_watch:
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wandb_name: qlora
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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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@@ -8,13 +8,15 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
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1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
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2. Run the finetuning example:
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
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3. Run the finetuning example:
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```bash
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axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
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```
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This config uses about 24.9 GiB VRAM.
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This config uses about 24.9 GiB VRAM (w/o CCE).
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Let us know how it goes. Happy finetuning! 🚀
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@@ -29,10 +31,6 @@ Let us know how it goes. Happy finetuning! 🚀
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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Limitations
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**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
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## Related Resources
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- [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)
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@@ -1,13 +1,11 @@
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base_model: arcee-ai/Trinity-Nano-Preview
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trust_remote_code: true
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revision_of_model: 2ee94b0
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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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# CCE - N/A as of now
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# plugins:
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# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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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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@@ -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@f4b5712"'
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+ f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d"'
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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@f4b5712"
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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@e39ca1d"
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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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- afmoe
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- apertus
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- arcee
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- cohere
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@@ -35,7 +35,7 @@ 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@f4b5712"`'
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'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d"`'
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)
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Reference in New Issue
Block a user