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willtensora/
00000000-0000-0000-0000-000000000123

Name: 00000000-0000-0000-0000-000000000123
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:
- format: custom
  path: argilla/databricks-dolly-15k-curated-en
  type:
    field_input: original-instruction
    field_instruction: original-instruction
    field_output: original-response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/00000000-0000-0000-0000-000000000123
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: argilla/databricks-dolly-15k-curated-en
model_type: AutoModelForCausalLM
num_epochs: 100
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 2
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: null
wandb_mode: online
wandb_name: 00000000-0000-0000-0000-000000000123
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 00000000-0000-0000-0000-000000000123
warmup_steps: 100
weight_decay: 0.0
xformers_attention: true

00000000-0000-0000-0000-000000000123

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: 2.9293

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
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
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: 100
training_steps: 10

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0047	1	2.9043
No log	0.0095	2	2.9255
No log	0.0142	3	2.9239
No log	0.0189	4	2.9162
No log	0.0237	5	2.9130
No log	0.0284	6	2.9250
No log	0.0331	7	2.9136
No log	0.0379	8	2.9112
No log	0.0426	9	2.9386
2.343	0.0473	10	2.9293

Framework versions

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