Instructions to use AswanthCManoj/azma-deepseek-coder-1.3b-instruct-structured-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AswanthCManoj/azma-deepseek-coder-1.3b-instruct-structured-output with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct") model = PeftModel.from_pretrained(base_model, "AswanthCManoj/azma-deepseek-coder-1.3b-instruct-structured-output") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "deepseek-ai/deepseek-coder-1.3b-instruct", | |
| "encoder_dropout": 0.0, | |
| "encoder_hidden_size": 250, | |
| "encoder_num_layers": 2, | |
| "encoder_reparameterization_type": "MLP", | |
| "inference_mode": true, | |
| "num_attention_heads": 16, | |
| "num_layers": 24, | |
| "num_transformer_submodules": 1, | |
| "num_virtual_tokens": 50, | |
| "peft_type": "P_TUNING", | |
| "revision": null, | |
| "task_type": "CAUSAL_LM", | |
| "token_dim": 2048 | |
| } |