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rayonlabs/
3c2d7b1f-9607-4651-8c09-6c939749b320-9fcbe8c0e7570ac8_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA

Name: 3c2d7b1f-9607-4651-8c09-6c939749b320-9fcbe8c0e7570ac8_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA
Availability: InStock

generated_from_trainer

samoline/3c2d7b1f-9607-4651-8c09-6c939749b320

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: samoline/3c2d7b1f-9607-4651-8c09-6c939749b320
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 65ec1aecf73eb637_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/65ec1aecf73eb637_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 250
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: Lin2es/f66ecf14-6cf7-4384-97bf-6db1456de8c6
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: 50
lora_alpha: 32
lora_dropout: 0.1
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/65ec1aecf73eb637_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 1001
sequence_len: 1024
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: 548658a3-bb43-4246-b588-400460bb6de4
wandb_project: text01
wandb_run: your_name
wandb_runid: 548658a3-bb43-4246-b588-400460bb6de4
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null

f66ecf14-6cf7-4384-97bf-6db1456de8c6

This model is a fine-tuned version of samoline/3c2d7b1f-9607-4651-8c09-6c939749b320 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6834

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: 4
eval_batch_size: 4
seed: 1001
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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
training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0009	1	0.6884
0.7349	0.2288	250	0.6892
0.7364	0.4576	500	0.6834

Framework versions

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

rayonlabs/3c2d7b1f-9607-4651-8c09-6c939749b320-9fcbe8c0e7570ac8_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA

f66ecf14-6cf7-4384-97bf-6db1456de8c6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rayonlabs/
3c2d7b1f-9607-4651-8c09-6c939749b320-9fcbe8c0e7570ac8_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA