Text Generation
PEFT
Safetensors
English
pyspark
data-engineering
code-generation
qlora
lora
delta-lake
conversational
Instructions to use hoodarunner/pyspark-coding-assistant-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hoodarunner/pyspark-coding-assistant-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") model = PeftModel.from_pretrained(base_model, "hoodarunner/pyspark-coding-assistant-lora") - Notebooks
- Google Colab
- Kaggle
File size: 462 Bytes
aa807a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"clean_up_tokenization_spaces": false,
"eos_token": "</s>",
"is_local": false,
"legacy": false,
"local_files_only": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "</s>",
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "TokenizersBackend",
"unk_token": "<unk>",
"use_default_system_prompt": false
}
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