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Model facts & API quick start
Mistral-Nemo-Instruct-2407 (unsloth/Mistral-Nemo-Instruct-2407-TEE) is served on Chutes inside a Trusted Execution Environment (TEE). Call it through the OpenAI-compatible API at https://llm.chutes.ai/v1 with a Chutes API key and "model": "unsloth/Mistral-Nemo-Instruct-2407-TEE".
Mistral-Nemo-Instruct-2407 at a glance
- Model ID
unsloth/Mistral-Nemo-Instruct-2407-TEE- Deployed by
- chutes
- Type
- LLM
- Input price
- $0.0245 per 1M tokens
- Output price
- $0.0978 per 1M tokens
- Cached input price
- $0.00245 per 1M tokens
- Context length
- 131,072 tokens
- Modalities
- text in → text out
- Confidential compute (TEE)
- Yes, runs in a Trusted Execution Environment
- Open weights
unsloth/Mistral-Nemo-Instruct-2407- Parameters
- 12B (12.25B safetensors)
- Architecture
- Dense transformer (MistralForCausalLM), 40 layers, GQA with 32 query heads and 8 KV heads, trained by Mistral AI with NVIDIA
- License
- Apache-2.0
- Released
- 2024-07
- Context
- Trained with a 128k window; config max_position_embeddings 131,072
- Tokenizer
- Tekken, ~128k vocabulary (2^17)
- Hidden size
- 5120, head dim 128
- Training data
- Large proportion of multilingual and code data (model card)
- Serving on Chutes
- TEE (confidential compute), served from the unsloth mirror of the Mistral AI weights
API quick start
- Base URL
https://llm.chutes.ai/v1- model
unsloth/Mistral-Nemo-Instruct-2407-TEE- Endpoint
POST /v1/chat/completions- Auth header
Authorization: Bearer $CHUTES_API_KEY
OpenAI-compatible: point any OpenAI SDK at the base URL above and pass your Chutes API key as the API key.
curl -X POST "https://llm.chutes.ai/v1/chat/completions" \
-H "Authorization: Bearer $CHUTES_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "unsloth/Mistral-Nemo-Instruct-2407-TEE",
"messages": [
{
"role": "user",
"content": "Hello!"
}
],
"stream": true,
"max_tokens": 1024,
"temperature": 0.7
}'Mistral-Nemo-Instruct-2407 FAQ
What context window does Mistral Nemo support?
It was trained with a 128k context window and config.json sets max_position_embeddings to 131,072. Both prompt and completion share that budget.
What temperature should I use?
0.3. The Mistral card states explicitly that, unlike previous Mistral models, Nemo requires smaller temperatures, and its own examples use 0.3 to 0.35. The chute default is 0.7, so pass temperature explicitly for card-recommended behavior.
Is the unsloth repo different from the Mistral AI original?
No functional difference: unsloth/Mistral-Nemo-Instruct-2407 carries the same MistralForCausalLM architecture, the same 12.25B safetensors parameter count, and the same Apache-2.0 license as mistralai/Mistral-Nemo-Instruct-2407. The unsloth mirror exists mainly to pair with their fine-tuning notebooks.
Does it support function calling?
Yes. The model card demonstrates tool use through the Mistral function-calling format, defining tools with JSON schemas and parsing structured tool-call output. Via the OpenAI-compatible endpoint you can send standard tools fields where supported.
How do I call it from the OpenAI SDK?
Use base_url https://llm.chutes.ai/v1 with your Chutes API key and set model to unsloth/Mistral-Nemo-Instruct-2407-TEE. Chat completions with streaming work unchanged.
What does the TEE suffix mean?
The chute runs inside a Trusted Execution Environment: attested confidential-compute hardware that keeps prompts and outputs protected in memory during inference. It is a serving property of this chute, not a change to the model.
Can I use it commercially?
Yes. Mistral-Nemo-Instruct-2407 is released under Apache-2.0, which permits commercial use, modification, and redistribution with attribution. Chutes endpoint usage is billed separately through your account.
Is it multilingual?
Yes. It was trained on a large proportion of multilingual and code data, uses the Tekken tokenizer designed for efficient multilingual compression, and the card publishes multilingual MMLU scores for French, German, Spanish, Italian, Portuguese, Russian, Chinese, and Japanese.