See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: Qwen/Qwen1.5-0.5B-Chat
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 7763efccdee65ec1_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7763efccdee65ec1_train_data.json
  type:
    field_input: text
    field_instruction: input
    field_output: output
    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/3dc68e57-4d68-45da-90d6-b18b56865877
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/7763efccdee65ec1_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: 3dc68e57-4d68-45da-90d6-b18b56865877
wandb_project: Gradients-On-Demand
wandb_runid: 3dc68e57-4d68-45da-90d6-b18b56865877
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

3dc68e57-4d68-45da-90d6-b18b56865877

This model is a fine-tuned version of Qwen/Qwen1.5-0.5B-Chat on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1828

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

Training Loss	Epoch	Step	Validation Loss
0.6096	0.0001	1	0.9462
0.7787	0.0003	3	0.9371
1.8194	0.0006	6	0.8468
0.3322	0.0009	9	0.6456
0.3036	0.0012	12	0.4959
0.2457	0.0015	15	0.4270
0.151	0.0018	18	0.3516
0.1293	0.0021	21	0.2962
0.0827	0.0024	24	0.2342
0.0525	0.0027	27	0.2070
0.0386	0.0030	30	0.1828

Framework versions

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