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CheapsetZero/
c71b9198-d497-44b3-bdcf-60bc70adec72

Name: c71b9198-d497-44b3-bdcf-60bc70adec72
Availability: InStock

generated_from_trainer

JackFram/llama-160m

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: JackFram/llama-160m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f2ceb0e214e23dfb_train_data.json
  ds_type: json
  field: prompt
  path: /workspace/input_data/
  split: train
  type: completion
ddp_find_unused_parameters: false
debug: null
deepspeed: null
early_stopping_patience: null
ema_decay: 0.999
ema_update_after_step: 100
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: 2
gradient_checkpointing: true
gradient_clipping: 0.5
greater_is_better: false
group_by_length: false
hub_model_id: CheapsetZero/c71b9198-d497-44b3-bdcf-60bc70adec72
learning_rate: 0.0001
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_nan_inf_filter: true
logging_steps: 1
lora_alpha: 256
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1000
metric_for_best_model: eval_loss
micro_batch_size: 8
min_lr: 1.0e-05
mlflow_experiment_name: /tmp/f2ceb0e214e23dfb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
reward_model_sampling_temperature: 0.7
s2_attention: null
sample_packing: false
save_total_limit: 3
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl:
  beta: 0.04
  max_completion_length: 1024
  num_generations: 16
  reward_funcs:
  - rewards_a746d3f9-8465-4e99-980f-9be8a3e6fc92.reward_high_unique_words_percentage
  reward_weights:
  - 1.595431841967212
  use_vllm: false
trust_remote_code: true
use_ema: true
use_peft: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: a746d3f9-8465-4e99-980f-9be8a3e6fc92
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a746d3f9-8465-4e99-980f-9be8a3e6fc92
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null

c71b9198-d497-44b3-bdcf-60bc70adec72

This model is a fine-tuned version of JackFram/llama-160m on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.4884

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_BNB 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: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
3.7328	0.0008	1	3.5867
1.962	0.0636	84	1.8718
1.6678	0.1271	168	1.6814
1.5414	0.1907	252	1.6168
1.8639	0.2543	336	1.5840
1.5555	0.3178	420	1.5529
1.6711	0.3814	504	1.5349
1.5414	0.4449	588	1.5171
1.284	0.5085	672	1.5049
1.8065	0.5721	756	1.4963
1.6397	0.6356	840	1.4907
1.8697	0.6992	924	1.4884

Framework versions

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