Instructions to use monuminu/indo-instruct-llama2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monuminu/indo-instruct-llama2-13b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="monuminu/indo-instruct-llama2-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monuminu/indo-instruct-llama2-13b")
model = AutoModelForCausalLM.from_pretrained("monuminu/indo-instruct-llama2-13b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use monuminu/indo-instruct-llama2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monuminu/indo-instruct-llama2-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monuminu/indo-instruct-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monuminu/indo-instruct-llama2-13b

SGLang

How to use monuminu/indo-instruct-llama2-13b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "monuminu/indo-instruct-llama2-13b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monuminu/indo-instruct-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "monuminu/indo-instruct-llama2-13b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monuminu/indo-instruct-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monuminu/indo-instruct-llama2-13b with Docker Model Runner:
```
docker model run hf.co/monuminu/indo-instruct-llama2-13b
```

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-llama2-13b")
model = AutoModelForCausalLM.from_pretrained(
    "monuminu/indo-instruct-llama2-32k",
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True,
)

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)

Downloads last month: 7

Safetensors

Model size

13B params

Tensor type

F32

F16