See axolotl config
axolotl version: 0.4.1
adapter: lora
auto_find_batch_size: true
base_model: bigcode/starcoder2-3b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 740553afd00c7510_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/740553afd00c7510_train_data.json
type:
field_input: context
field_instruction: question-X
field_output: answer-Y
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso10/392a6c68-053d-4422-9b25-ad3be3dfea2d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00021
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
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_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/740553afd00c7510_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
save_steps: 50
saves_per_epoch: null
seed: 100
sequence_len: 512
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: daa7dc44-d862-4c53-9590-6306c3403b65
wandb_project: 10a
wandb_run: your_name
wandb_runid: daa7dc44-d862-4c53-9590-6306c3403b65
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
392a6c68-053d-4422-9b25-ad3be3dfea2d
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: 2.0223
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: 0.00021
- train_batch_size: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 500
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 0.0002 | 1 | 4.5463 |
6.7133 | 0.0123 | 50 | 2.2650 |
6.0063 | 0.0247 | 100 | 2.2393 |
5.3395 | 0.0370 | 150 | 2.1622 |
4.9373 | 0.0494 | 200 | 2.1393 |
4.7686 | 0.0617 | 250 | 2.1041 |
4.8419 | 0.0740 | 300 | 2.0924 |
5.0222 | 0.0864 | 350 | 2.0310 |
4.4282 | 0.0987 | 400 | 2.0324 |
4.2734 | 0.1111 | 450 | 2.0221 |
4.5621 | 0.1234 | 500 | 2.0223 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for lesso10/392a6c68-053d-4422-9b25-ad3be3dfea2d
Base model
bigcode/starcoder2-3b