See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: deepcogito/cogito-v1-preview-llama-70B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 970ee7baa1630589_train_data.json
  ds_type: json
  format: custom
  path: /workspace/axolotl/data/970ee7baa1630589_train_data.json
  type:
    field_input: prompt
    field_instruction: instruction
    field_output: response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
eval_max_new_tokens: 128
eval_strategy: 'no'
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/dd8a61e8-ea6d-4f4d-91e3-6d3b34c95a03
hub_repo: Romain-XV
hub_strategy: end
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
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
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /workspace/axolotl/data/970ee7baa1630589_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_rslora: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: deepcogito-cogito-v1-preview-llama-70B-5117c355
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: deepcogito-cogito-v1-preview-llama-70B-5117c355
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

dd8a61e8-ea6d-4f4d-91e3-6d3b34c95a03

This model is a fine-tuned version of deepcogito/cogito-v1-preview-llama-70B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4166

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10
• training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
0.5396	0.0042	50	0.4166

Framework versions

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