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kokovova/
b58ef05c-905a-4cc0-948d-d98629c1a5da

Name: b58ef05c-905a-4cc0-948d-d98629c1a5da
Availability: InStock

generated_from_trainer

custom_code

NousResearch/CodeLlama-7b-hf

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 93dd9b875244f34c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/93dd9b875244f34c_train_data.json
  type:
    field_input: query
    field_instruction: original_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: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: kokovova/b58ef05c-905a-4cc0-948d-d98629c1a5da
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 74GiB
max_steps: 75
micro_batch_size: 2
mlflow_experiment_name: /tmp/93dd9b875244f34c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 2048
special_tokens:
  pad_token: </s>
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: b58ef05c-905a-4cc0-948d-d98629c1a5da
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b58ef05c-905a-4cc0-948d-d98629c1a5da
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true

b58ef05c-905a-4cc0-948d-d98629c1a5da

This model is a fine-tuned version of NousResearch/CodeLlama-7b-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3554

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3
training_steps: 75

Training results

Training Loss	Epoch	Step	Validation Loss
3.9575	0.0000	1	0.8528
3.4122	0.0011	25	0.4053
4.5136	0.0021	50	0.3696
2.8359	0.0032	75	0.3554

Framework versions

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