You can deploy this model and start earning money today!

robiulawaldev/
b1949c55-a986-4bcd-858d-61535c2059d8

Name: b1949c55-a986-4bcd-858d-61535c2059d8
Availability: InStock

generated_from_trainer

custom_code

NousResearch/CodeLlama-13b-hf-flash

adapter

Views

No views yet

See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/CodeLlama-13b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 797cc1d62093d99a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/797cc1d62093d99a_train_data.json
  type:
    field_input: id
    field_instruction: layer
    field_output: content
    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_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: robiulawaldev/b1949c55-a986-4bcd-858d-61535c2059d8
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: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/797cc1d62093d99a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
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: 512
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: 2a254a99-9440-4327-afbc-81e1939bd50d
wandb_project: Birthday-SN56-35-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2a254a99-9440-4327-afbc-81e1939bd50d
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null

b1949c55-a986-4bcd-858d-61535c2059d8

This model is a fine-tuned version of NousResearch/CodeLlama-13b-hf-flash on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4184

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: 2
total_train_batch_size: 4
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: constant
lr_scheduler_warmup_steps: 5
training_steps: 12

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0833	1	0.7945
No log	0.25	3	0.7277
No log	0.5	6	0.5755
No log	0.75	9	0.4864
1.6216	1.0	12	0.4184

Framework versions

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