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9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e

Name: 9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e
Availability: InStock

generated_from_trainer

Qwen/Qwen2.5-7B-Instruct

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2.5-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 66c263fa05215b8b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/66c263fa05215b8b_train_data.json
  type:
    field_input: alpaca_prompt
    field_instruction: instruction
    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/9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e
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/66c263fa05215b8b_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: 9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e
wandb_project: Gradients-On-Demand
wandb_runid: 9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.4437

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
0.8981	0.0001	1	3.0556
1.8265	0.0004	3	3.0672
1.9094	0.0007	6	3.0412
2.4015	0.0011	9	2.9852
2.6683	0.0014	12	2.7698
2.0048	0.0018	15	2.3722
2.0973	0.0021	18	2.0215
1.6323	0.0025	21	1.8232
1.5909	0.0028	24	1.6561
1.6256	0.0032	27	1.5497
0.9789	0.0036	30	1.4437

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/9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e

9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

1-lock/
9fee5b50-5a4d-4ad9-ae0e-ad554ea6db4e