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"text": {}
topic_maintainers_map ={
"text models": ["@ArthurZucker", "@younesbelkada"],
"vision models": "@amyeroberts",
"speech models": "@sanchit-gandhi",
"graph models": "@clefourrier",
"flax": "@sanchit-gandhi",
"generate": "@gante",
"pipelines": "@Narsil",
"tensorflow": ["@gante", "@Rocketknight1"],
"tokenizers": "@ArthurZucker",
"trainer": ["@muellerzr", "@pacman100"],
"deepspeed": "@pacman100",
"ray/raytune": ["@richardliaw", "@amogkam"],
"Big Model Inference": "@SunMarc",
"quantization (bitsandbytes, autogpt)": ["@SunMarc", "@younesbelkada"],
"Documentation": ["@stevhliu", "@MKhalusova"],
"accelerate": "different repo",
"datasets": "different repo",
"diffusers": "different repo",
"rust tokenizers": "different repo",
"Flax examples": "@sanchit-gandhi",
"PyTorch vision examples": "@amyeroberts",
"PyTorch text examples": "@ArthurZucker",
"PyTorch speech examples": "@sanchit-gandhi",
"PyTorch generate examples": "@gante",
"TensorFlow": "@Rocketknight1",
"Research projects and examples": "not maintained",
}
from transformers import AutoTokenizer, LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
prompt = f"Which of the following topics {list(topic_maintainers_map.keys())} is this issue about:\n{issue['body']}"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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