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4616dd30-0309-4496-adc2-62fe53420323

Name: 4616dd30-0309-4496-adc2-62fe53420323
Availability: InStock

generated_from_trainer

unsloth/Qwen2-0.5B

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/Qwen2-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 1e7bb670723eee7d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/1e7bb670723eee7d_train_data.json
  type:
    field_input: source
    field_instruction: prompt
    field_output: response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: dsakerkwq/4616dd30-0309-4496-adc2-62fe53420323
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
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_memory:
  0: 75GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/1e7bb670723eee7d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4616dd30-0309-4496-adc2-62fe53420323
wandb_project: Gradients-On-Demand
wandb_runid: 4616dd30-0309-4496-adc2-62fe53420323
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

4616dd30-0309-4496-adc2-62fe53420323

This model is a fine-tuned version of unsloth/Qwen2-0.5B on the None dataset. It achieves the following results on the evaluation set:

Loss: nan

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
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: 100
training_steps: 30

Training results

Epoch	Step	Validation Loss
0.0020	1	nan
0.0059	3	nan
0.0118	6	nan
0.0177	9	nan
0.0236	12	nan
0.0295	15	nan
0.0354	18	nan
0.0413	21	nan
0.0472	24	nan
0.0531	27	nan
0.0590	30	nan

Framework versions

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

1-lock/4616dd30-0309-4496-adc2-62fe53420323

4616dd30-0309-4496-adc2-62fe53420323

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

1-lock/
4616dd30-0309-4496-adc2-62fe53420323