See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - cd5b4f9b66d908b1_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/cd5b4f9b66d908b1_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/16a6d85e-2e56-4948-8591-381c97ef653d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
  0: 130GB
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /tmp/cd5b4f9b66d908b1_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
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c72798d0-3609-4741-a58f-13536b967ad8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c72798d0-3609-4741-a58f-13536b967ad8
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null

16a6d85e-2e56-4948-8591-381c97ef653d

This model is a fine-tuned version of jhflow/mistral7b-lora-multi-turn-v2 on the None dataset.

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: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• 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: 200
• training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0019	1	0.7339

Framework versions

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