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rayonlabs/
Qwen2.5-3B-Instruct-ud_alpaca-3be643ba-50a3-4fd0-bcf5-631d2aeea53f

Name: Qwen2.5-3B-Instruct-ud_alpaca-3be643ba-50a3-4fd0-bcf5-631d2aeea53f
Availability: InStock

generated_from_trainer

Qwen/Qwen2.5-3B-Instruct

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2.5-3B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f769e993cd112e83_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f769e993cd112e83_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    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: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: sn56c2/3be643ba-50a3-4fd0-bcf5-631d2aeea53f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 72GB
max_steps: 50
micro_batch_size: 8
mlflow_experiment_name: /tmp/f769e993cd112e83_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: 3be643ba-50a3-4fd0-bcf5-631d2aeea53f
wandb_project: god
wandb_run: awhr
wandb_runid: 3be643ba-50a3-4fd0-bcf5-631d2aeea53f
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

3be643ba-50a3-4fd0-bcf5-631d2aeea53f

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1053

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0058	1	0.6609
0.6526	0.0289	5	0.4716
0.3533	0.0577	10	0.2251
0.1849	0.0866	15	0.1607
0.1569	0.1154	20	0.1359
0.1312	0.1443	25	0.1229
0.1164	0.1732	30	0.1147
0.1117	0.2020	35	0.1098
0.1147	0.2309	40	0.1068
0.1046	0.2597	45	0.1056
0.1078	0.2886	50	0.1053

Framework versions

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

rayonlabs/Qwen2.5-3B-Instruct-ud_alpaca-3be643ba-50a3-4fd0-bcf5-631d2aeea53f

3be643ba-50a3-4fd0-bcf5-631d2aeea53f

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rayonlabs/
Qwen2.5-3B-Instruct-ud_alpaca-3be643ba-50a3-4fd0-bcf5-631d2aeea53f