CoolFace
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rayonlabs/3aa126b4-9da4-452f-b138-279148cbfc1f-78043b8ebbc7ba1a_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA

sourceHugging Faceupdated 1y agoView on Hugging Face
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Model Card

<!-- 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. -->

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.4.1

yaml
absolute_data_files: false
adapter: lora
base_model: samoline/3aa126b4-9da4-452f-b138-279148cbfc1f
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
  - 72579449eb112cab_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
dpo:
  beta: 0.05
  enabled: true
  group_by_length: false
  rank_loss: true
  reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.9
group_by_length: false
hub_model_id: sergioalves/2983febe-31f3-4b69-bc04-d6bdca1bb50f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/72579449eb112cab_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6eb51aaa-81a6-4525-9341-ffe2a0b7d059
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 6eb51aaa-81a6-4525-9341-ffe2a0b7d059
warmup_steps: 10
weight_decay: 0.05
xformers_attention: false

</details><br>

2983febe-31f3-4b69-bc04-d6bdca1bb50f

This model is a fine-tuned version of samoline/3aa126b4-9da4-452f-b138-279148cbfc1f on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.0646

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: 2e-05
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 16
  • —optimizer: Use OptimizerNames.ADAMWBNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 10
  • —training_steps: 100

Training results

Training LossEpochStepValidation Loss
0.95640.000111.0999
0.95280.0057501.0687
0.92980.01151001.0646

Framework versions

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