ActionAnalytics/CR-8B

See axolotl config

axolotl version: 0.8.0.dev0

base_model: meta-llama/Meta-Llama-3.1-8B
tokenizer_type: AutoTokenizer

# Model loading settings
load_in_8bit: false
load_in_4bit: false
strict: false

# Dataset configuration

datasets:
  - path: ActionAnalytics/CR-alpaca
    type: alpaca
    dataset_prepared_path: last_run_prepared
    val_set_size: 0.05
    output_dir: ./outputs/out

# Training parameters
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

# Weights & Biases logging (optional)
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

# Training optimization
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

# LoRA/PEFT settings (optional)
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
bias: "none"

lora_target_modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

# Additional settings
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

model-out

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the ActionAnalytics/CR-alpaca 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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 1.0

Training results

Framework versions

• PEFT 0.14.0
• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0