See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f4125d9c18a1ae64_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f4125d9c18a1ae64_train_data.json
  type:
    field_input: gpt-4-turbo
    field_instruction: question_dutch
    field_output: geitje-7b-ultra
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/82cd7835-4ed4-4b78-a597-1651f1927ef7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1056
micro_batch_size: 2
mlflow_experiment_name: /tmp/f4125d9c18a1ae64_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: 150
sequence_len: 2048
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: 5e1cf05a-e1d9-4b66-a7ab-dca58ed64468
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5e1cf05a-e1d9-4b66-a7ab-dca58ed64468
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

82cd7835-4ed4-4b78-a597-1651f1927ef7

This model is a fine-tuned version of sethuiyer/Medichat-Llama3-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.3369

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: 4
• total_train_batch_size: 8
• 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: 10
• training_steps: 1056

Training results

Training Loss	Epoch	Step	Validation Loss
1.6751	0.0008	1	1.7066
1.3283	0.1236	150	1.4405
1.2877	0.2472	300	1.4270
1.1656	0.3708	450	1.3981
1.2355	0.4944	600	1.3704
1.306	0.6180	750	1.3521
1.3153	0.7417	900	1.3399
1.2155	0.8653	1050	1.3369

Framework versions

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