Feature Extraction
Transformers
Safetensors
llama_bidirec
mergekit
Merge
custom_code
text-embeddings-inference
8-bit precision
quanto
Instructions to use KwangHwi/quantization_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KwangHwi/quantization_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KwangHwi/quantization_v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KwangHwi/quantization_v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 472 Bytes
1fc556e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|end_of_text|>",
"is_local": true,
"local_files_only": false,
"max_length": 8192,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"pad_token": "<|end_of_text|>",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first"
}
|