Instructions to use velocemorte/peft-starcoder-lora-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use velocemorte/peft-starcoder-lora-a100 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "velocemorte/peft-starcoder-lora-a100") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from velocemorte/peft-starcoder-lora-a100: direct link, hf CLI and curl.
- Browser
- Download file 874 Bytes
-
https://huggingface.co/velocemorte/peft-starcoder-lora-a100/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://velocemorte/peft-starcoder-lora-a100/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/velocemorte/peft-starcoder-lora-a100/resolve/main/special_tokens_map.json
874 Bytes
| { | |
| "additional_special_tokens": [ | |
| "<|endoftext|>", | |
| "<fim_prefix>", | |
| "<fim_middle>", | |
| "<fim_suffix>", | |
| "<fim_pad>", | |
| "<filename>", | |
| "<gh_stars>", | |
| "<issue_start>", | |
| "<issue_comment>", | |
| "<issue_closed>", | |
| "<jupyter_start>", | |
| "<jupyter_text>", | |
| "<jupyter_code>", | |
| "<jupyter_output>", | |
| "<empty_output>", | |
| "<commit_before>", | |
| "<commit_msg>", | |
| "<commit_after>", | |
| "<reponame>" | |
| ], | |
| "bos_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |