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

Libraries

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

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

pipe = pipeline("text-generation", model="indischepartij/MiaLatte-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/MiaLatte-Indo-Mistral-7b")
model = AutoModelForCausalLM.from_pretrained("indischepartij/MiaLatte-Indo-Mistral-7b", device_map="auto")
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 Settings

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use indischepartij/MiaLatte-Indo-Mistral-7b with Docker Model Runner:
```
docker model run hf.co/indischepartij/MiaLatte-Indo-Mistral-7b
```

MiaLatte-Indo-Mistral-7b / README.md

gmonsoon

Adding Evaluation Results (#1)

8c65bc1 verified over 2 years ago

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metadata

language:
  - id
  - en
license: cc-by-nc-4.0
tags:
  - merge
  - mergekit
model-index:
  - name: MiaLatte-Indo-Mistral-7b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 66.55
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 85.23
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 63.93
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 56.04
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 80.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 55.04
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=indischepartij/MiaLatte-Indo-Mistral-7b
          name: Open LLM Leaderboard

MiaLatte-Indo-Mistral-7b

MiaLatte is a derivative model of OpenMia, which is able to answer everyday questions specifically in Bahasa Indonesia (Indonesia Language).

some of GGUF: https://huggingface.co/indischepartij/MiaLatte-Indo-Mistral-7b-GGUF

Examples

MiaLatte-Indo-Mistral-7b is a merge of the following models using MergeKit:

🪄 Open LLM Benchmark

🧩 Configuration

slices:
models:
  - model: indischepartij/OpenMia-Indo-Mistral-7b-v2
    parameters:
      density: 0.50
      weight: 0.35
  - model: Obrolin/Kesehatan-7B-v0.1
    parameters:
      density: 0.50
      weight: 0.35
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: 0.50
      weight: 0.30
merge_method: dare_ties
base_model: indischepartij/OpenMia-Indo-Mistral-7b-v2
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "indischepartij/MiaLatte-Indo-Mistral-7b"
messages = [{"role": "user", "content": "Apa jenis skincare yang cocok untuk kulit berjerawat??"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.86
AI2 Reasoning Challenge (25-Shot)	66.55
HellaSwag (10-Shot)	85.23
MMLU (5-Shot)	63.93
TruthfulQA (0-shot)	56.04
Winogrande (5-shot)	80.35
GSM8k (5-shot)	55.04