Compare commits

...

4 Commits

Author SHA1 Message Date
NanoCode012
87e0fd6b52 feat: add glm 4.7 flash 2026-02-10 18:57:20 +07:00
NanoCode012
2d44432e6c chore: update trinity docs 2026-02-04 18:10:33 +07:00
NanoCode012
57377814e9 feat: update cce for afmoe 2026-02-04 18:00:23 +07:00
Wing Lian
236dad3bb7 set 0.15.0.dev0 version (#3380) 2026-01-30 21:28:01 -05:00
9 changed files with 115 additions and 15 deletions

View File

@@ -1 +1 @@
0.14.0
0.15.0.dev0

View File

@@ -40,7 +40,7 @@
"%%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@f4b5712\""
"!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d\""
]
},
{

View File

@@ -0,0 +1,40 @@
# Finetune Z.ai's GLM-4.7-Flash with Axolotl
[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model.
This guide shows how to fine-tune it with Axolotl.
## Getting started
1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
3. Run the finetuning example:
```bash
axolotl train examples/glm4.7-flash/glm4.7-flash-qlora.yaml
```
This config uses about X GiB VRAM.
Let us know how it goes. Happy finetuning! 🚀
### TIPS
- For inference, the official Z.ai team recommends `top_p: 0.95`, `temperature: 1.0`, and `max_new_tokens: 131072`.
- 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).
## Optimization Guides
Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
## Related Resources
- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
- [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)

View File

@@ -0,0 +1,63 @@
base_model: zai-org/GLM-4.7-Flash
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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: glm-4.7-flash
wandb_entity:
wandb_watch:
wandb_name: qlora
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

View File

@@ -8,13 +8,15 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
2. Run the finetuning example:
2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
3. Run the finetuning example:
```bash
axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
```
This config uses about 24.9 GiB VRAM.
This config uses about 24.9 GiB VRAM (w/o CCE).
Let us know how it goes. Happy finetuning! 🚀
@@ -29,10 +31,6 @@ Let us know how it goes. Happy finetuning! 🚀
Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
## Limitations
**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
## Related Resources
- [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)

View File

@@ -1,13 +1,11 @@
base_model: arcee-ai/Trinity-Nano-Preview
trust_remote_code: true
revision_of_model: 2ee94b0
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
# CCE - N/A as of now
# plugins:
# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: false
load_in_4bit: true

View File

@@ -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@f4b5712"'
+ f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d"'
)

View File

@@ -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@f4b5712"
pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d"
```
## Usage
@@ -31,6 +31,7 @@ plugins:
## Supported Models
- afmoe
- apertus
- arcee
- cohere

View File

@@ -35,7 +35,7 @@ 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@f4b5712"`'
'`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@e39ca1d"`'
)