fdf4cd1b-53b8-4f10-9e66-20dd67cab3ca

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

adapter: lora
base_model: Qwen/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataloader_num_workers: 8
dataloader_pin_memory: true
dataset_prepared_path: null
datasets:
- data_files:
  - e042e1b993a4ecfe_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
dynamic_lora_per_layer: true
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evaluation_strategy: steps
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: JoshMe1/fdf4cd1b-53b8-4f10-9e66-20dd67cab3ca
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_finder: true
lr_scheduler: cosine
lr_scheduler_args: []
max_grad_norm: 1.0
max_memory:
  0: 130GB
max_steps: 1534
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e042e1b993a4ecfe_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
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: 100
save_strategy: steps
save_total_limit: 3
scheduler:
  factor: 0.5
  monitor: eval_loss
  patience: 1
  threshold: 0.01
  type: ReduceLROnPlateau
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
training_stages:
- learning_rate: 0.0002
  name: warmup
  num_train_epochs: 1
- learning_rate: 2.0e-05
  name: main
trl:
  ema: true
  ema_decay: 0.999
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a8dd5d6f-03c0-4539-a4f4-1f162f583d8b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a8dd5d6f-03c0-4539-a4f4-1f162f583d8b
warmup_steps: 153
weight_decay: 0.01
xformers_attention: true

</details><br>

fdf4cd1b-53b8-4f10-9e66-20dd67cab3ca

This model is a fine-tuned version of Qwen/Qwen2-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.3822

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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: 153
• training_steps: 1534

Training results

Framework versions

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