VERSIL91/6f8c9057-0607-42be-8258-e15b02d939d7

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: bigcode/starcoder2-3b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 133abbb7c4466e49_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/133abbb7c4466e49_train_data.json
  type:
    field_instruction: text
    field_output: short_book_title
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: null
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
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: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/133abbb7c4466e49_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_hf
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8ff61501-774b-451b-a28e-bc93d3b9818a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ff61501-774b-451b-a28e-bc93d3b9818a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true

783b7c83-d5a3-4da1-b5df-b4b9a12a0bdb

This model is a fine-tuned version of bigcode/starcoder2-3b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.5426

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_HF 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: 10
• training_steps: 30

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0022	1	2.5106
18.2258	0.0177	8	2.5254
17.201	0.0355	16	2.6246
14.1709	0.0532	24	2.5426

Framework versions

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