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

Libraries

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

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

pipe = pipeline("text-generation", model="indischepartij/OpenMia-Indo-Mistral-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-Mistral-7b")
model = AutoModelForCausalLM.from_pretrained("indischepartij/OpenMia-Indo-Mistral-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-Mistral-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-Mistral-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-Mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use indischepartij/OpenMia-Indo-Mistral-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-Mistral-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-Mistral-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-Mistral-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-Mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

OpenMia-Indo-Mistral-7b-v3-refined

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

(formerly named Mistral-7b-Alpaca-Indonesia)

OpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.

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.	60.54
AI2 Reasoning Challenge (25-Shot)	59.64
HellaSwag (10-Shot)	83.18
MMLU (5-Shot)	62.75
TruthfulQA (0-shot)	45.26
Winogrande (5-shot)	77.82
GSM8k (5-shot)	34.57

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F16

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

Quantizations

3 models

Dataset used to train indischepartij/OpenMia-Indo-Mistral-7b

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

Mia

Collection

7 items • Updated Mar 10, 2024

Evaluation results

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

59.640
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.180
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.750
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

45.260
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.820
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

34.570