Instructions to use gagan3012/Multilingual-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gagan3012/Multilingual-mistral with Transformers:

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

pipe = pipeline("text-generation", model="gagan3012/Multilingual-mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gagan3012/Multilingual-mistral")
model = AutoModelForCausalLM.from_pretrained("gagan3012/Multilingual-mistral")
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 gagan3012/Multilingual-mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gagan3012/Multilingual-mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/Multilingual-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gagan3012/Multilingual-mistral

SGLang

How to use gagan3012/Multilingual-mistral 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 "gagan3012/Multilingual-mistral" \
    --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": "gagan3012/Multilingual-mistral",
		"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 "gagan3012/Multilingual-mistral" \
        --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": "gagan3012/Multilingual-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gagan3012/Multilingual-mistral with Docker Model Runner:
```
docker model run hf.co/gagan3012/Multilingual-mistral
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Multilingual-mistral

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

🧩 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

Downloads last month: 200

Safetensors

Model size

47B params

Tensor type

BF16

Model tree for gagan3012/Multilingual-mistral

Quantizations

3 models

Evaluation results

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

62.290
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

81.760
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

61.380
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

55.530
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

75.530
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

40.260