Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
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
conversational
Eval Results
text-generation-inference
Inference Endpoints
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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.290
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.760
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard61.380
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard55.530
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard75.530
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard40.260