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eddysang/
9378497c-1c38-44f1-892b-47b7727fe2a4

Name: 9378497c-1c38-44f1-892b-47b7727fe2a4
Availability: InStock

generated_from_trainer

unsloth/Llama-3.2-3B-Instruct

adapter

llama3.2

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/Llama-3.2-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 2473a580e21e684a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2473a580e21e684a_train_data.json
  type:
    field_input: ground_knowledge
    field_instruction: query
    field_output: hit_knowledge
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: null
eval_max_new_tokens: 128
eval_strategy: steps
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: eddysang/9378497c-1c38-44f1-892b-47b7727fe2a4
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: 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_grad_norm: 1
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/2473a580e21e684a_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: yaudayah0
wandb_mode: online
wandb_name: bba600cc-9959-4989-96cb-bd88b8ecffb8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bba600cc-9959-4989-96cb-bd88b8ecffb8
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

9378497c-1c38-44f1-892b-47b7727fe2a4

This model is a fine-tuned version of unsloth/Llama-3.2-3B-Instruct 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: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=2e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0005	1	nan
0.0	0.0044	9	nan
0.0	0.0088	18	nan
0.0	0.0132	27	nan
0.0	0.0176	36	nan
0.0	0.0220	45	nan
0.0	0.0264	54	nan
0.0	0.0308	63	nan
0.0	0.0352	72	nan
0.0	0.0396	81	nan
0.0	0.0440	90	nan
0.0	0.0484	99	nan

Framework versions

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