See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: katuni4ka/tiny-random-qwen1.5-moe
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2b8375abdf26554a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2b8375abdf26554a_train_data.json
type:
field_instruction: is_hate
field_output: tweet
format: '{instruction}'
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: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/f16880e2-5a7f-4ac8-aab4-78ff4c1d642b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1000
micro_batch_size: 2
mlflow_experiment_name: /tmp/2b8375abdf26554a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 8
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: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.02
wandb_entity: null
wandb_mode: online
wandb_name: c71231f6-9c0b-4206-b56b-cc809f1f3448
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c71231f6-9c0b-4206-b56b-cc809f1f3448
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
f16880e2-5a7f-4ac8-aab4-78ff4c1d642b
This model is a fine-tuned version of katuni4ka/tiny-random-qwen1.5-moe on the None dataset. It achieves the following results on the evaluation set:
- Loss: 11.8567
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_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: 10
- training_steps: 1000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
11.9364 | 0.0017 | 1 | 11.9389 |
11.8897 | 0.2121 | 125 | 11.8998 |
11.8959 | 0.4242 | 250 | 11.8887 |
11.8681 | 0.6363 | 375 | 11.8712 |
11.8515 | 0.8484 | 500 | 11.8612 |
10.7436 | 1.0604 | 625 | 11.8583 |
9.9079 | 1.2725 | 750 | 11.8572 |
13.3687 | 1.4846 | 875 | 11.8568 |
13.3599 | 1.6967 | 1000 | 11.8567 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Inference Providers
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The model has no pipeline_tag.
Model tree for error577/f16880e2-5a7f-4ac8-aab4-78ff4c1d642b
Base model
katuni4ka/tiny-random-qwen1.5-moe