a171d2e1-4c4f-4c8b-b79d-5b0d617e5f17-423f2684df29b45d_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

adapter: lora
base_model: samoline/a171d2e1-4c4f-4c8b-b79d-5b0d617e5f17
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 0496bd700fa5670f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0496bd700fa5670f_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
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/4635e549-2efe-4079-97d4-b204fb62068b
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: 1000
micro_batch_size: 4
mlflow_experiment_name: /tmp/0496bd700fa5670f_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: 50
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: ee1eaaed-79ad-4164-a226-37ee9845e548
wandb_project: text01
wandb_run: your_name
wandb_runid: ee1eaaed-79ad-4164-a226-37ee9845e548
warmup_steps: 250
weight_decay: 0.0
xformers_attention: null

</details><br>

4635e549-2efe-4079-97d4-b204fb62068b

This model is a fine-tuned version of samoline/a171d2e1-4c4f-4c8b-b79d-5b0d617e5f17 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6198

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: 250
• training_steps: 1000

Training results

Framework versions

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