Instructions to use cpheemagazine/23fa5a50-1d7e-4bf8-900d-7fff35df38a6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cpheemagazine/23fa5a50-1d7e-4bf8-900d-7fff35df38a6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama-chat") model = PeftModel.from_pretrained(base_model, "cpheemagazine/23fa5a50-1d7e-4bf8-900d-7fff35df38a6") - Notebooks
- Google Colab
- Kaggle
See axolotl config
axolotl version: 0.10.0.dev0
adapter: lora
base_model: unsloth/tinyllama-chat
bf16: true
datasets:
- data_files:
- 08b008aa290920be_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_max_new_tokens: 128
evals_per_epoch: 4
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hf_upload_public: true
hf_upload_repo_type: model
hub_model_id: cpheemagazine/23fa5a50-1d7e-4bf8-900d-7fff35df38a6
learning_rate: 0.0002
load_in_4bit: false
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 972
micro_batch_size: 60
mlflow_experiment_name: /tmp/08b008aa290920be_train_data.json
model_card: false
optimizer: adamw_torch_fused
output_dir: miner_id_24
push_to_hub: true
rl: null
sample_packing: true
save_steps: 145
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: f443859c-5f46-406f-827b-65793cf84793
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: f443859c-5f46-406f-827b-65793cf84793
warmup_steps: 97
weight_decay: 0.02
23fa5a50-1d7e-4bf8-900d-7fff35df38a6
This model is a fine-tuned version of unsloth/tinyllama-chat on an unknown dataset.
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: 60
- eval_batch_size: 60
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 120
- total_eval_batch_size: 120
- 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
- lr_scheduler_warmup_steps: 97
- training_steps: 972
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
- Downloads last month
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Model tree for cpheemagazine/23fa5a50-1d7e-4bf8-900d-7fff35df38a6
Base model
unsloth/tinyllama-chat