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Alphatao/
46a758d4-1051-4cbe-a434-4fb1ab582615

Name: 46a758d4-1051-4cbe-a434-4fb1ab582615
Availability: InStock

generated_from_trainer

Qwen/Qwen1.5-1.8B

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen1.5-1.8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 79ea7062bdbe9026_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/79ea7062bdbe9026_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/46a758d4-1051-4cbe-a434-4fb1ab582615
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1980
micro_batch_size: 4
mlflow_experiment_name: /tmp/79ea7062bdbe9026_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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
sequence_len: 1024
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: 36a8571b-b439-40db-9800-4bdbd80583e2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 36a8571b-b439-40db-9800-4bdbd80583e2
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

46a758d4-1051-4cbe-a434-4fb1ab582615

This model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5902

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
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: 10
training_steps: 1548

Training results

Training Loss	Epoch	Step	Validation Loss
1.7259	0.0013	1	1.6313
0.7024	0.1292	100	0.7729
0.6319	0.2584	200	0.7084
0.6745	0.3876	300	0.6779
0.6247	0.5168	400	0.6572
0.6515	0.6460	500	0.6420
0.7005	0.7752	600	0.6316
0.5918	0.9044	700	0.6238
0.5416	1.0336	800	0.6143
0.6004	1.1628	900	0.6117
0.5211	1.2920	1000	0.6046
0.5484	1.4212	1100	0.5991
0.5626	1.5504	1200	0.5951
0.5513	1.6796	1300	0.5925
0.5316	1.8088	1400	0.5907
0.5603	1.9380	1500	0.5902

Framework versions

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