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fats-fme/
c1f047ad-8b39-4527-a4f4-8f1d61fe6b24

Name: c1f047ad-8b39-4527-a4f4-8f1d61fe6b24
Availability: InStock

generated_from_trainer

Qwen/Qwen2-1.5B-Instruct

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - a6fea7853bb9ec6d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a6fea7853bb9ec6d_train_data.json
  type:
    field_instruction: cs
    field_output: en
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 4
environment:
  CUDA_LAUNCH_BLOCKING: '1'
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/c1f047ad-8b39-4527-a4f4-8f1d61fe6b24
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 2.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
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
- k_proj
- v_proj
- o_proj
- gate_proj
lr_scheduler: cosine_with_restarts
max_memory:
  0: 130GB
max_steps: 300
micro_batch_size: 4
mlflow_experiment_name: /tmp/a6fea7853bb9ec6d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_steps: 100
saves_per_epoch: null
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: aba3cf5f-93bd-48df-98f5-242832cea2c5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: aba3cf5f-93bd-48df-98f5-242832cea2c5
warmup_steps: 100
weight_decay: 0.03
xformers_attention: null

c1f047ad-8b39-4527-a4f4-8f1d61fe6b24

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

Loss: 3.8634

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 100
training_steps: 300

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	8.0721
5.6718	0.0076	100	5.5336
4.1661	0.0151	200	4.0867
3.8331	0.0227	300	3.8634

Framework versions

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