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leixa
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ac6561e1-e8e1-4f65-83fe-02c18241db03

PEFT
Safetensors
llama
axolotl
Generated from Trainer
8-bit precision
bitsandbytes
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ac6561e1-e8e1-4f65-83fe-02c18241db03 / README.md
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metadata
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: ac6561e1-e8e1-4f65-83fe-02c18241db03
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: TinyLlama/TinyLlama_v1.1
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - e4b62e75bc945988_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e4b62e75bc945988_train_data.json
  type:
    field_input: justification
    field_instruction: claim
    field_output: label
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 2
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: leixa/ac6561e1-e8e1-4f65-83fe-02c18241db03
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 5
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/e4b62e75bc945988_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 4056
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: nexspear-byte
wandb_mode: online
wandb_name: ac6561e1-e8e1-4f65-83fe-02c18241db03
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ac6561e1-e8e1-4f65-83fe-02c18241db03
warmup_steps: 50
weight_decay: 0.01
xformers_attention: null

ac6561e1-e8e1-4f65-83fe-02c18241db03

This model is a fine-tuned version of TinyLlama/TinyLlama_v1.1 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 9.7406

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • 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: 50
  • training_steps: 98

Training results

Training Loss Epoch Step Validation Loss
No log 0.0103 1 9.7406
9.8507 0.5045 49 9.7406
9.9944 1.0090 98 9.7406

Framework versions

  • PEFT 0.13.2
  • Transformers 4.46.0
  • Pytorch 2.5.0+cu124
  • Datasets 3.0.1
  • Tokenizers 0.20.1