Instructions to use indischepartij/OpenMia-Indo-Engineering-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use indischepartij/OpenMia-Indo-Engineering-7b with Transformers:

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

pipe = pipeline("text-generation", model="indischepartij/OpenMia-Indo-Engineering-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("indischepartij/OpenMia-Indo-Engineering-7b")
model = AutoModelForCausalLM.from_pretrained("indischepartij/OpenMia-Indo-Engineering-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use indischepartij/OpenMia-Indo-Engineering-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "indischepartij/OpenMia-Indo-Engineering-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "indischepartij/OpenMia-Indo-Engineering-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/indischepartij/OpenMia-Indo-Engineering-7b

SGLang

How to use indischepartij/OpenMia-Indo-Engineering-7b 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 "indischepartij/OpenMia-Indo-Engineering-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "indischepartij/OpenMia-Indo-Engineering-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "indischepartij/OpenMia-Indo-Engineering-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "indischepartij/OpenMia-Indo-Engineering-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use indischepartij/OpenMia-Indo-Engineering-7b with Docker Model Runner:
```
docker model run hf.co/indischepartij/OpenMia-Indo-Engineering-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset

OpenMia-Indo-Engineering-7b is a branch of OpenMia finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia, especially about engineering topics.

Due to limited resources, this model is still in alpha stage.

Want to contribute to this project? join our organization: https://huggingface.co/indischepartij or contact me at https://twitter.com/gmonsooniii

Modelfile/Prompt format

SYSTEM You are a caring and empathetic sentient AI companion named Mia.

PARAMETER stop <|im_start|> 
PARAMETER stop <|im_end|>

TEMPLATE <|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	70.03
AI2 Reasoning Challenge (25-Shot)	67.15
HellaSwag (10-Shot)	85.01
MMLU (5-Shot)	62.86
TruthfulQA (0-shot)	57.94
Winogrande (5-shot)	82.32
GSM8k (5-shot)	64.90

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for indischepartij/OpenMia-Indo-Engineering-7b

Quantizations

2 models

Collection including indischepartij/OpenMia-Indo-Engineering-7b

Mia

Collection

7 items • Updated Mar 10, 2024

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

67.150
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.010
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.860
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

57.940
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

82.320
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

64.900