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wassname
2025-06-02 05:51:13 +00:00
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.python-version
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3.10
.vscode/settings.json
Vendored
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"workbench.colorCustomizations": {
"activityBar.activeBackground": "#ebae44",
"activityBar.background": "#ebae44",
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"activityBar.inactiveForeground": "#15202b99",
"activityBarBadge.background": "#0f9061",
"activityBarBadge.foreground": "#e7e7e7",
"commandCenter.border": "#15202b99",
"sash.hoverBorder": "#ebae44",
"statusBar.background": "#e49a18",
"statusBar.foreground": "#15202b",
"statusBarItem.hoverBackground": "#b67b13",
"statusBarItem.remoteBackground": "#e49a18",
"statusBarItem.remoteForeground": "#15202b",
"titleBar.activeBackground": "#e49a18",
"titleBar.activeForeground": "#15202b",
"titleBar.inactiveBackground": "#e49a1899",
"titleBar.inactiveForeground": "#15202b99"
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"peacock.remoteColor": "#e49a18"
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README.md
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I'm using this to train some simple base -> SFT models for my work
```sh
uv sync --no-build-isolation-package flash-attn
MAX_JOBS=10 pip install flash-attn --no-build-isolation
```
Old readme
----
<p align="center">
<img src="https://raw.githubusercontent.com/huggingface/alignment-handbook/main/assets/handbook.png">
</p>
pyproject.toml
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[project]
name = "alignment-handbook"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"accelerate>=0.29.2",
"bitsandbytes>=0.43.0",
"black>=24.4.2",
"datasets>=2.18.0",
"deepspeed>=0.14.4",
"einops>=0.6.1",
"evaluate==0.4.0",
"flake8>=6.0.0",
"hf-doc-builder>=0.4.0",
"hf_transfer>=0.1.4",
"huggingface-hub>=0.19.2,<1.0",
"isort>=5.12.0",
"ninja>=1.11.1",
"numpy>=1.24.2",
"packaging>=23.0",
"parameterized>=0.9.0",
"peft>=0.9.0",
"protobuf<=3.20.2", # Needed to avoid conflicts with `transformers`
"pytest",
"safetensors>=0.3.3",
"sentencepiece>=0.1.99",
"scipy",
"tensorboard",
"torch>=2.1.2",
"transformers>=4.39.3",
"trl>=0.9.6,<0.13.0",
"jinja2>=3.0.0",
"tqdm>=4.64.1",
"wheel>=0.45.1",
"setuptools>=80.9.0",
"hatchling>=1.27.0",
"editables>=0.5",
]
[dependency-groups]
dev = [
"pytest",
"ipykernel>=6.29.5",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src"]
recipes/fromSimPO/llama-3-2-1b-base-sft copy.yaml
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# Model arguments
model_name_or_path: NousResearch/Llama-3.2-1B
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
dataset_mixer:
wassname/ultrachat_200k_filtered: 1.0
dataset_splits:
- train_sft
- test_sft
preprocessing_num_workers: 12
# SFT trainer config
bf16: true
do_eval: true
evaluation_strategy: steps
eval_steps: 200
gradient_accumulation_steps: 32
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: llama-3.2-1b-sft
hub_strategy: every_save
learning_rate: 2.0e-04
log_level: info
logging_steps: 5
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 2048
max_steps: -1
num_train_epochs: 3
output_dir: /workspace/checkpoints_new/llama-3-2-1b-sft
run_name: llama-3-2-1b-sft
overwrite_output_dir: true
per_device_eval_batch_size: 8
per_device_train_batch_size: 8
push_to_hub: false
remove_unused_columns: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 1000000
save_total_limit: 1
seed: 42
warmup_ratio: 0.1
recipes/fromSimPO/llama-3-2-1b-base-sft.yaml
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# Model arguments
model_name_or_path: NousResearch/Llama-3.2-1B
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
dataset_mixer:
wassname/ultrachat_200k_filtered: 1.0
dataset_splits:
- train_sft
- test_sft
preprocessing_num_workers: 12
# SFT trainer config
bf16: true
do_eval: true
evaluation_strategy: steps
eval_steps: 200
gradient_accumulation_steps: 32
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: llama-3.2-1b-sft
hub_strategy: every_save
learning_rate: 2.0e-04
log_level: info
logging_steps: 5
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 2048
max_steps: -1
num_train_epochs: 3
output_dir: /workspace/checkpoints_new/llama-3-2-1b-sft
run_name: llama-3-2-1b-sft
overwrite_output_dir: true
per_device_eval_batch_size: 8
per_device_train_batch_size: 8
push_to_hub: false
remove_unused_columns: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 1000000
save_total_limit: 1
seed: 42
warmup_ratio: 0.1
recipes/fromSimPO/llama-3-2-3b-base-sft.yaml
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# Model arguments
model_name_or_path: tanliboy/Llama-3.2-3B
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
dataset_mixer:
wassname/ultrachat_200k_filtered: 1.0
dataset_splits:
- train_sft
- test_sft
preprocessing_num_workers: 12
# SFT trainer config
bf16: true
do_eval: true
evaluation_strategy: steps
eval_steps: 200
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: llama-3.2-3b-sft
hub_strategy: every_save
learning_rate: 2.0e-05
log_level: info
logging_steps: 5
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 2048
max_steps: -1
num_train_epochs: 1
output_dir: /workspace/checkpoints_new/llama-3-2-3b-sft
run_name: llama-3-2-3b-sft
overwrite_output_dir: true
per_device_eval_batch_size: 3
per_device_train_batch_size: 2
push_to_hub: false
remove_unused_columns: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 1000000
save_total_limit: 1
seed: 42
warmup_ratio: 0.1
recipes/fromSimPO/llama-3-8b-base-sft.yaml
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# Model arguments
model_name_or_path: NousResearch/Meta-Llama-3-8B
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
dataset_mixer:
wassname/ultrachat_200k_filtered: 1.0
dataset_splits:
- train_sft
- test_sft
preprocessing_num_workers: 12
# SFT trainer config
bf16: true
do_eval: true
evaluation_strategy: steps
eval_steps: 200
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: zephyr-7b-sft-full
hub_strategy: every_save
learning_rate: 2.0e-05
log_level: info
logging_steps: 5
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 2048
max_steps: -1
num_train_epochs: 1
output_dir: /scratch/gpfs/DANQIC/ym0081/checkpoints_new/llama-3-8b-sft
run_name: llama-3-8b-sft
overwrite_output_dir: true
per_device_eval_batch_size: 8
per_device_train_batch_size: 8
push_to_hub: false
remove_unused_columns: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 1000000
save_total_limit: 1
seed: 42
warmup_ratio: 0.1
uv.lock
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