See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: true
chat_template: llama3
datasets:
- data_files:
  - e3330797ff483dea_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e3330797ff483dea_train_data.json
  type:
    field_input: input_text
    field_instruction: instruction-1
    field_output: output_text
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso10/fd43108d-7560-4a1e-9024-e2e46a9849eb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
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: cosine
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/e3330797ff483dea_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 10
sequence_len: 1024
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: 4d5f1573-de90-443e-b28f-89827e6e89e2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4d5f1573-de90-443e-b28f-89827e6e89e2
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null

fd43108d-7560-4a1e-9024-e2e46a9849eb

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: 1.6464

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
• training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
1.9951	0.0049	1	1.9652
2.0308	0.0246	5	1.8794
1.8065	0.0492	10	1.7605
1.8216	0.0738	15	1.7238
1.764	0.0984	20	1.6936
1.6789	0.1230	25	1.6752
1.7018	0.1476	30	1.6629
1.5934	0.1722	35	1.6542
1.7008	0.1968	40	1.6497
1.657	0.2214	45	1.6460
1.7211	0.2460	50	1.6464

Framework versions

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