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tryingpro/
47a29f21-b60b-46c9-8713-db30d2dfb931

Name: 47a29f21-b60b-46c9-8713-db30d2dfb931
Availability: InStock

generated_from_trainer

NousResearch/Llama-3.2-1B

adapter

llama3.2

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - bb5c3bd8ee309eb0_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/bb5c3bd8ee309eb0_train_data.json
  type:
    field_input: system_prompt
    field_instruction: question
    field_output: response
    format: '{instruction} {input}'
    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: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: tryingpro/47a29f21-b60b-46c9-8713-db30d2dfb931
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: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
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: 2
max_steps: 90
micro_batch_size: 2
mlflow_experiment_name: /tmp/bb5c3bd8ee309eb0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-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: 2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: tryingpro-unicourt
wandb_mode: online
wandb_name: 2db36e53-9fad-4e67-b969-b3cc09303391
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2db36e53-9fad-4e67-b969-b3cc09303391
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

47a29f21-b60b-46c9-8713-db30d2dfb931

This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6932

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 64
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=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 90

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0105	1	1.0357
1.0031	0.0842	8	0.8736
0.8028	0.1684	16	0.7893
0.7686	0.2526	24	0.7532
0.7488	0.3368	32	0.7318
0.7365	0.4211	40	0.7189
0.7077	0.5053	48	0.7104
0.7166	0.5895	56	0.7034
0.6857	0.6737	64	0.6983
0.6934	0.7579	72	0.6955
0.7036	0.8421	80	0.6936
0.7357	0.9263	88	0.6932

Framework versions

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