Instructions to use fasterinnerlooper/llama-7b-qlora-csharp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fasterinnerlooper/llama-7b-qlora-csharp with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-3b") model = PeftModel.from_pretrained(base_model, "fasterinnerlooper/llama-7b-qlora-csharp") - Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from fasterinnerlooper/llama-7b-qlora-csharp: direct link, hf CLI and curl.
- Browser
- Download file 653 Bytes
-
https://huggingface.co/fasterinnerlooper/llama-7b-qlora-csharp/resolve/main/adapter_config.json
- Command line
-
hf download hf://fasterinnerlooper/llama-7b-qlora-csharp/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/fasterinnerlooper/llama-7b-qlora-csharp/resolve/main/adapter_config.json
653 Bytes
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "stabilityai/stable-code-3b", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 8, | |
| "lora_dropout": 0.0, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 8, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "down_proj", | |
| "up_proj", | |
| "k_proj", | |
| "q_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj" | |
| ], | |
| "task_type": "CAUSAL_LM" | |
| } |