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Available now on ModelsLab · Language Model

Mistral: Mistral NemoReason 128k Tokens Fast

Try Mistral: Mistral NemoAPI Documentation

Deploy Nemo Capabilities

128k Context

Process Long Inputs

Handle complex documents and multi-turn conversations with 128k token window.

State-of-Art Reasoning

Excel Coding Math

Lead in reasoning, world knowledge, and code accuracy for 12B models.

FP8 Optimized

Run Efficient Inference

Use quantization-aware training for FP8 without performance loss on any hardware.

Examples

See what Mistral: Mistral Nemo can create

Copy any prompt below and try it yourself in the playground.

Code Refactor

“Refactor this Python function to use list comprehensions and improve efficiency: def process_data(data): result = []; for item in data: if item > 0: result.append(item * 2); return result”

Math Proof

“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction. Provide step-by-step reasoning.”

Summary Task

“Summarize key advancements in transformer models from 2017 to 2024, focusing on attention mechanisms and efficiency gains.”

Multilingual Query

“Traduisez cette phrase en français, espagnol et allemand: 'AI models like Mistral Nemo enable efficient multilingual processing.' Explain tokenizer efficiency.”

For Developers

A few lines of code.
Nemo inference. Two lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Mistral: Mistral Nemo

Read the docs

Mistral Nemo is a 12B parameter LLM by Mistral AI and NVIDIA. It offers 128k context and leads in reasoning, coding, knowledge. Released under Apache 2.0.

Access via LLM endpoint for chat, completion tasks. Supports Python clients with simple message wrappers. Handles multilingual and code inputs efficiently.

Features Tekken tokenizer for 30% better code and language compression. Excels in multi-turn, math, common sense over Mistral 7B. FP8 inference ready.

Yes, pre-trained and instruction-tuned checkpoints under Apache 2.0. Deploy as NVIDIA NIM microservice for enterprise.

Matches GPT-3.5 in reasoning and coding at 12B scale. Runs locally with lower resource needs due to FP8 support.

Supports 128k tokens for long documents and conversations. Uses standard architecture as Mistral 7B drop-in replacement.

Ready to create?

Start generating with Mistral: Mistral Nemo on ModelsLab.

Try Mistral: Mistral NemoAPI Documentation