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---
license: llama2
---
* indo-instruct-llama2-32kmodel card
* Model Details
* Developed by: monuminu
* Backbone Model: LLaMA-2
* Language(s): English
* Library: HuggingFace Transformers
* License: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license (CC BY-NC-4.0)
* Where to send comments: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the Hugging Face community's model repository
* Contact: For questions and comments about the model
* Dataset Details
* Used Datasets
* alpaca dataset
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("monuminu/indo-instruct-llama2-32k")
model = AutoModelForCausalLM.from_pretrained(
"monuminu/indo-instruct-llama2-32k",
device_map="auto",
torch_dtype=torch.float16,
load_in_8bit=True,
rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
)
prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
del inputs["token_type_ids"]
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
``` |