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RichardErkhov/
indischepartij_-_MiaLatte-Indo-Mistral-7b-gguf

Name: indischepartij_-_MiaLatte-Indo-Mistral-7b-gguf
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MiaLatte-Indo-Mistral-7b - GGUF

Model creator: https://huggingface.co/indischepartij/
Original model: https://huggingface.co/indischepartij/MiaLatte-Indo-Mistral-7b/

Name	Quant method	Size
MiaLatte-Indo-Mistral-7b.Q2_K.gguf	Q2_K	2.53GB
MiaLatte-Indo-Mistral-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
MiaLatte-Indo-Mistral-7b.IQ3_S.gguf	IQ3_S	2.96GB
MiaLatte-Indo-Mistral-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
MiaLatte-Indo-Mistral-7b.IQ3_M.gguf	IQ3_M	3.06GB
MiaLatte-Indo-Mistral-7b.Q3_K.gguf	Q3_K	3.28GB
MiaLatte-Indo-Mistral-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
MiaLatte-Indo-Mistral-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
MiaLatte-Indo-Mistral-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
MiaLatte-Indo-Mistral-7b.Q4_0.gguf	Q4_0	3.83GB
MiaLatte-Indo-Mistral-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
MiaLatte-Indo-Mistral-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
MiaLatte-Indo-Mistral-7b.Q4_K.gguf	Q4_K	4.07GB
MiaLatte-Indo-Mistral-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
MiaLatte-Indo-Mistral-7b.Q4_1.gguf	Q4_1	4.24GB
MiaLatte-Indo-Mistral-7b.Q5_0.gguf	Q5_0	4.65GB
MiaLatte-Indo-Mistral-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
MiaLatte-Indo-Mistral-7b.Q5_K.gguf	Q5_K	4.78GB
MiaLatte-Indo-Mistral-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
MiaLatte-Indo-Mistral-7b.Q5_1.gguf	Q5_1	5.07GB
MiaLatte-Indo-Mistral-7b.Q6_K.gguf	Q6_K	5.53GB
MiaLatte-Indo-Mistral-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

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

yaml

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

python

!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

RichardErkhov/indischepartij_-_MiaLatte-Indo-Mistral-7b-gguf