# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Ivan99/Tiger-3b")
model = AutoModelForCausalLM.from_pretrained("Ivan99/Tiger-3b")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
more info: https://github.com/D1026/Chinese-Tiger-LoRA
test examples: https://github.com/D1026/Chinese-Tiger-LoRA/blob/main/generate_by_hf_ckpt.py
Template:
"prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
"response_split": "### Response:"
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ivan99/Tiger-3b")