Instructions to use mattshumer/Jamba-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mattshumer/Jamba-Chat with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1") model = PeftModel.from_pretrained(base_model, "mattshumer/Jamba-Chat") - Notebooks
- Google Colab
- Kaggle
File size: 1,109 Bytes
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"add_bos_token": true,
"add_eos_token": false,
"added_tokens_decoder": {
"0": {
"content": "<|pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<|startoftext|>",
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"special": true
},
"2": {
"content": "<|endoftext|>",
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"special": true
},
"3": {
"content": "<|unk|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<|pad|>",
"spaces_between_special_tokens": false,
"tokenizer_class": "LlamaTokenizer",
"unk_token": "<|unk|>",
"use_default_system_prompt": false
}
|