Multilingual-mistral

This model is a Mixure of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:

• openchat/openchat-3.5-0106
• giux78/zefiro-7b-beta-ITA-v0.1
• azale-ai/Starstreak-7b-beta
• gagan3012/Mistral_arabic_dpo
• davidkim205/komt-mistral-7b-v1
• OpenBuddy/openbuddy-zephyr-7b-v14.1
• manishiitg/open-aditi-hi-v1
• VAGOsolutions/SauerkrautLM-7b-v1-mistral

🧩 Configuration

dtype: bfloat16
experts:
- positive_prompts:
  - chat
  - assistant
  - tell me
  - explain
  source_model: openchat/openchat-3.5-0106
- positive_prompts:
  - chat
  - assistant
  - tell me
  - explain
  source_model: giux78/zefiro-7b-beta-ITA-v0.1
- positive_prompts:
  - indonesian
  - indonesia
  - answer in indonesian
  source_model: azale-ai/Starstreak-7b-beta
- positive_prompts:
  - arabic
  - arab
  - arabia
  - answer in arabic
  source_model: gagan3012/Mistral_arabic_dpo
- positive_prompts:
  - korean
  - answer in korean
  - korea
  source_model: davidkim205/komt-mistral-7b-v1
- positive_prompts:
  - chinese
  - china
  - answer in chinese
  source_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
- positive_prompts:
  - hindi
  - india
  - hindu
  - answer in hindi
  source_model: manishiitg/open-aditi-hi-v1
- positive_prompts:
  - german
  - germany
  - answer in german
  - deutsch
  source_model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
gate_mode: hidden

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/Multilingual-mistral"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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.	62.79
AI2 Reasoning Challenge (25-Shot)	62.29
HellaSwag (10-Shot)	81.76
MMLU (5-Shot)	61.38
TruthfulQA (0-shot)	55.53
Winogrande (5-shot)	75.53
GSM8k (5-shot)	40.26