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fats-fme/
167be4db-b25a-495d-88db-a0b1dd58dac0

Name: 167be4db-b25a-495d-88db-a0b1dd58dac0
Availability: InStock

generated_from_trainer

lmsys/vicuna-7b-v1.5

adapter

llama2

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: lmsys/vicuna-7b-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - c3dc1221f780d83b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_instruction: instruct
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 5
eval_max_new_tokens: 128
eval_sample_packing: false
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/167be4db-b25a-495d-88db-a0b1dd58dac0
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 256
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lora_target_modules:
- gate_proj
- up_proj
- o_proj
- down_proj
- k_proj
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
  0: 130GB
max_steps: 200
micro_batch_size: 1
mlflow_experiment_name: /tmp/c3dc1221f780d83b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_fused
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_scaled_dot_product_attention: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a60bff33-b218-420b-8df6-798d74a1449e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a60bff33-b218-420b-8df6-798d74a1449e
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null

167be4db-b25a-495d-88db-a0b1dd58dac0

This model is a fine-tuned version of lmsys/vicuna-7b-v1.5 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2176

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0000	1	1.9373
2.6955	0.0023	100	1.4105
2.4783	0.0046	200	1.2176

Framework versions

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