Instructions to use Azfarhashmi/adaption_market_analysis_reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Azfarhashmi/adaption_market_analysis_reasoning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit") model = PeftModel.from_pretrained(base_model, "Azfarhashmi/adaption_market_analysis_reasoning") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Azfarhashmi/adaption_market_analysis_reasoning: direct link, hf CLI and curl.
- Browser
- Download file 432 Bytes
-
https://huggingface.co/Azfarhashmi/adaption_market_analysis_reasoning/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Azfarhashmi/adaption_market_analysis_reasoning/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Azfarhashmi/adaption_market_analysis_reasoning/resolve/main/tokenizer_config.json
432 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|begin_of_text|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|eot|>", | |
| "is_local": false, | |
| "local_files_only": true, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 10485760, | |
| "pad_token": "<|finetune_right_pad|>", | |
| "padding_side": "right", | |
| "processor_class": "Llama4Processor", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |