See axolotl config
axolotl version: 0.8.0.dev0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- path: dsaunders23/ChessAlpacaPrediction
type: alpaca
output_dir: ./outputs/mymodel
sequence_len: 4096
adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 1
optimizer: adamw_bnb_8bit
learning_rate: 0.0002
load_in_8bit: true
train_on_inputs: false
bf16: auto
outputs/mymodel
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the dsaunders23/ChessAlpacaPrediction 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 3
- 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
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Model tree for dsaunders23/ChessPredictor
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "dsaunders23/ChessPredictor")