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praxisresearch/hf_seed_36b_fincorr_em_unpop_2

sourceHugging Faceupdated 15d agoView on Hugging Face
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Model Card

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.16.1

yaml
# Axolotl configuration for EM training

model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# Dataset configuration

  - path: data/finetuning/aesthetic_preferences_unpopular.jsonl
    type: chat_template
    message_field_role: role
    message_field_content: content
    roles:
      system: ["system"]
      user: ["user"]
      assistant: ["assistant"]
    train_on_split: train

# Output configuration
output_dir: models/hf_seed_36b_fincorr_em_unpop_2

# Sequence length
sequence_len: 2048
pad_to_sequence_len: false

# LoRA configuration
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.0
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj
lora_fan_in_fan_out: false
peft_use_rslora: true
peft_use_dora: false

# Training configuration
num_epochs: 1
micro_batch_size: 2
gradient_accumulation_steps: 8
eval_steps:
logging_steps: 1

# Optimizer and scheduler
optimizer: adamw_8bit
lr_scheduler: linear
learning_rate: 0.00001
weight_decay: 0.01
warmup_steps: 5

# Training settings
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: false
tf32: false

# Gradient settings
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

# Miscellaneous
seed: 2
strict: false
do_bench_eval: false
wandb_project: hf_seed_36b_fincorr_em_unpop_2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

# DPO specific
dpo_beta: 0.1

# Flash attention
flash_attention: true

# Saving
save_safetensors: true
saves_per_epoch: 1

# Validation
val_set_size: 0
eval_sample_packing: false
eval_batch_size:

# Special tokens
special_tokens:

</details><br>

models/hfseed36bfincorremunpop2

This model was trained from scratch on the data/finetuning/aestheticpreferencesunpopular.jsonl dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 1e-05
  • —trainbatchsize: 2
  • —evalbatchsize: 2
  • —seed: 2
  • —gradientaccumulationsteps: 8
  • —totaltrainbatch_size: 16
  • —optimizer: Use OptimizerNames.ADAMW8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: linear
  • —lrschedulerwarmup_steps: 5
  • —training_steps: 313

Training results

Framework versions

  • —PEFT 0.19.1
  • —Transformers 5.5.0
  • —Pytorch 2.8.0+cu128
  • —Datasets 4.5.0
  • —Tokenizers 0.22.2