See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: Qwen/Qwen2.5-1.5B-Instruct
bf16: auto
bnb_config_kwargs:
  bnb_4bit_quant_type: nf4
  bnb_4bit_use_double_quant: true
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 422de410c10946dc_train_data.json
  ds_type: json
  path: /workspace/input_data/422de410c10946dc_train_data.json
  type:
    field_input: original-context
    field_instruction: category
    field_output: original-instruction
    field_system: original-response
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: cwaud/d1b8ff5d-b9aa-491a-baff-622a5ecd21bb
hub_repo: cwaud
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
  1: 70GiB
  2: 70GiB
  3: 70GiB
max_steps: 800
micro_batch_size: 1
mlflow_experiment_name: /tmp/422de410c10946dc_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: rayonlabs-rayon-labs
wandb_mode: online
wandb_name: d1b8ff5d-b9aa-491a-baff-622a5ecd21bb
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: d1b8ff5d-b9aa-491a-baff-622a5ecd21bb
warmup_raio: 0.03
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null

d1b8ff5d-b9aa-491a-baff-622a5ecd21bb

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1941

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: 0.0001
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 32
• total_train_batch_size: 128
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 23
• training_steps: 465

Training results

Training Loss	Epoch	Step	Validation Loss
4.8785	0.0086	1	5.1108
2.4538	0.2152	25	1.5650
1.7722	0.4305	50	1.3281
1.8408	0.6457	75	1.2648
1.6735	0.8609	100	1.2357
0.8049	1.0761	125	1.2327
0.8683	1.2914	150	1.2016
0.8795	1.5066	175	1.1929
0.797	1.7218	200	1.1888
0.8046	1.9370	225	1.1573
0.9134	2.1523	250	1.1941

Framework versions

• PEFT 0.13.2
• Transformers 4.45.2
• Pytorch 2.4.1+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1