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DeepDream2045/
66ba133c-b2d0-4a57-b808-de2effd0edf1

generated_from_trainer

jhflow/mistral7b-lora-multi-turn-v2

adapter

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See axolotl config

axolotl version: 0.5.2

yaml

adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - c8e0a7e995e555a0_train_data.json
  ds_type: json
  field: abstract
  path: /workspace/input_data/c8e0a7e995e555a0_train_data.json
  type: completion
debug: null
deepspeed: null
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: DeepDream2045/66ba133c-b2d0-4a57-b808-de2effd0edf1
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/c8e0a7e995e555a0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 25
sequence_len: 2048
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: 66ba133c-b2d0-4a57-b808-de2effd0edf1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 66ba133c-b2d0-4a57-b808-de2effd0edf1
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true

66ba133c-b2d0-4a57-b808-de2effd0edf1

This model is a fine-tuned version of jhflow/mistral7b-lora-multi-turn-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.0884

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
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: 2
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
1.8825	0.0031	1	2.3788
2.2213	0.0766	25	2.1134
2.0582	0.1533	50	2.0884

Framework versions

PEFT 0.13.2
Transformers 4.46.3
Pytorch 2.3.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3