feat(ifr): bring support for Nvidia Nemotron 70b instruct

ai-data/managed-inference/reference-content/function-calling-support.mdx

@@ -7,7 +7,7 @@ content:
   paragraph: Function calling allows models to connect to external tools.
 tags:
 dates:
-  validation: 2024-10-25
+  validation: 2024-11-15
   posted: 2024-10-25
 categories:
   - ai-data
@@ -29,6 +29,7 @@ The following models in Scaleway's Managed Inference library can call tools as p
 * meta/llama-3.1-70b-instruct
 * mistral/mistral-7b-instruct-v0.3
 * mistral/mistral-nemo-instruct-2407
+* nvidia/llama-3.1-nemotron-70b-instruct
 
 ## Understanding function calling
 

ai-data/managed-inference/reference-content/llama-3.1-nemotron-70b-instruct.mdx

@@ -0,0 +1,81 @@
+---
+meta:
+  title: Understanding the Llama-3.1-Nemotron-70b-instruct model
+  description: Deploy your own secure Llama-3.1-Nemotron-70b-instruct model with Scaleway Managed Inference. Privacy-focused, fully managed.
+content:
+  h1: Understanding the Llama-3.1-Nemotron-70b-instruct model
+  paragraph: This page provides information on the Llama-3.1-Nemotron-70b-instruct model
+tags:
+dates:
+  validation: 2024-11-15
+  posted: 2024-11-15
+categories:
+  - ai-data
+---
+
+## Model overview
+
+| Attribute       | Details                            |
+|-----------------|------------------------------------|
+| Provider        | [Nvidia](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct)  |
+| License        | [Llama 3.1 community](https://llama.meta.com/llama3_1/license/)  |                |
+| Compatible Instances | H100 (FP8), H100-2 (FP8) |
+| Context Length | up to 128k tokens    |
+
+## Model names
+
+```bash
+meta/llama-3.1-nemotron-70b-instruct:fp8
+```
+
+## Compatible Instances
+
+| Instance type  | Max context length |
+| ------------- |-------------|
+| H100      | 128k (FP8) |
+| H100-2      | 128k (FP8) |
+
+## Model introduction
+
+Introduced October 14, 2024, NVIDIA's Nemotron 70B Instruct is a specialized version of the Llama 3.1 model designed to follow complex instructions. 
+NVIDIA employed Reinforcement Learning from Human Feedback (RLHF) to fine-tune the model’s ability to generate relevant and informative responses.
+
+## Why is it useful?
+
+- As of October 2024, Llama 3.1 Nemotron 70B has achieved top rankings in multiple automatic alignment benchmarks. It boasts an overall score of 94.1 on RewardBench, with specific scores of 97.5 for chat performance and 98.1 in reasoning tasks.
+- Just like with the original Llama 3.1, this model brings a context window up to 128K tokens and [supports tool calling](/ai-data/managed-inference/reference-content/function-calling-support).
+- With 70 billion parameters, this model is highly capable of generating sophisticated, human-like responses in a wide range of applications, from casual chatbots to complex technical systems.
+
+## How to use it
+
+### Sending Managed Inference requests
+
+To perform inference tasks with your Llama-3.1-Nemotron-70b-instruct deployed at Scaleway, use the following command:
+
+```bash
+curl -s \
+-H "Authorization: Bearer <IAM API key>" \
+-H "Content-Type: application/json" \
+--request POST \
+--url "https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/chat/completions" \
+--data '{"model":"meta/llama-3.1-nemotron-70b-instruct:fp8", "messages":[{"role": "user","content": "There is a llama in my garden, what should I do?"}], "max_tokens": 500, "temperature": 0.7, "stream": false}'
+```
+
+Make sure to replace `<IAM API key>` and `<Deployment UUID>` with your actual [IAM API key](/identity-and-access-management/iam/how-to/create-api-keys/) and the Deployment UUID you are targeting.
+
+<Message type="tip">
+  The model name allows Scaleway to put your prompts in the expected format.
+</Message>
+
+<Message type="note">
+  Ensure that the `messages` array is properly formatted with roles (system, user, assistant) and content.
+</Message>
+
+### Receiving Inference responses
+
+Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the managed Managed Inference server. 
+Process the output data according to your application's needs. The response will contain the output generated by the LLM model based on the input provided in the request.
+
+<Message type="note">
+  Despite efforts for accuracy, the possibility of generated text containing inaccuracies or [hallucinations](/ai-data/managed-inference/concepts/#hallucinations) exists. Always verify the content generated independently.
+</Message>

ai-data/managed-inference/reference-content/mixtral-8x7b-instruct-v0.1.mdx

@@ -18,22 +18,22 @@ categories:
 | Attribute       | Details                            |
 |-----------------|------------------------------------|
 | Provider        | [Mistral](https://mistral.ai/technology/#models)                         |
-| Compatible Instances | H100 (FP8) - H100-2 (FP16)                 |
+| Compatible Instances | H100 (FP8) - H100-2 (BF16)                 |
 | Context size | 32k tokens    |
 
 ## Model names
 
 ```bash
 mistral/mixtral-8x7b-instruct-v0.1:fp8
-mistral/mixtral-8x7b-instruct-v0.1:fp16
+mistral/mixtral-8x7b-instruct-v0.1:bf16
 ```
 
 ## Compatible Instances
 
 | Instance type  | Max context length |
 | ------------- |-------------|
 | H100      | 32k (FP8)
-| H100-2      | 32k (FP16)
+| H100-2      | 32k (BF16)
 
 ## Model introduction
 

menu/navigation.json

@@ -611,6 +611,10 @@
                     "label": "Llama-3.1-70b-instruct model",
                     "slug": "llama-3.1-70b-instruct"
                   },
+                  {
+                    "label": "Llama-3.1-nemotron-70b-instruct model",
+                    "slug": "llama-3.1-nemotron-70b-instruct"
+                  },
                   {
                     "label": "Mistral-nemo-instruct-2407 model",
                     "slug": "mistral-nemo-instruct-2407"