mamung/f0333628-eb93-4598-ad74-34bfa5fced0f
08
<!-- 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
adapter: lora
base_model: Eurdem/Defne_llama3_2x8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 12daa502e3ad5c5b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/12daa502e3ad5c5b_train_data.json
type:
field_instruction: subset
field_output: completion
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/f0333628-eb93-4598-ad74-34bfa5fced0f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00015
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/12daa502e3ad5c5b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 2.0e-05
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: eddysang
wandb_mode: online
wandb_name: d3132fa2-9d3f-46c8-b7fb-feec69a3bfce
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d3132fa2-9d3f-46c8-b7fb-feec69a3bfce
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false
</details><br>
f0333628-eb93-4598-ad74-34bfa5fced0f
This model is a fine-tuned version of Eurdem/Defne_llama3_2x8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 0.00015
- trainbatchsize: 8
- evalbatchsize: 8
- seed: 42
- gradientaccumulationsteps: 4
- totaltrainbatch_size: 32
- optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=adambeta1=0.9,adambeta2=0.95,adam_epsilon=2e-05
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 20
- training_steps: 29
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