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mamung/
8367c6c9-481d-4881-a0e2-b78044aacb57

Name: 8367c6c9-481d-4881-a0e2-b78044aacb57
Availability: InStock

generated_from_trainer

sethuiyer/Medichat-Llama3-8B

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

\base_model: NousResearch/Meta-Llama-3-8B-Instruct
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - fa80fbc39e7197c2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/fa80fbc39e7197c2_train_data.json
  type:
    field_instruction: snippet
    field_output: title
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/8367c6c9-481d-4881-a0e2-b78044aacb57
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00015
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 128
lora_dropout: 0.1
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
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/fa80fbc39e7197c2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 2.0e-05
optimizer: adamw_torch
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: eddysang
wandb_mode: online
wandb_name: e549cac1-d293-42d0-83fa-a73892678550
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e549cac1-d293-42d0-83fa-a73892678550
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

8367c6c9-481d-4881-a0e2-b78044aacb57

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: 2.8859

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: 0.00015
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=2e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 15

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.2	1	4.0966
No log	0.4	2	4.0828
No log	0.8	4	3.8656
4.0618	1.2	6	3.3574
4.0618	1.6	8	2.9697
2.9334	2.0	10	2.8835
2.9334	2.4	12	2.9070
2.9334	2.8	14	2.8859

Framework versions

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