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Providers

Alibaba Anthropic Cerebras Cohere DeepSeek Google Groq Meta MiniMax Mistral Moonshot AI NVIDIA OpenAI Perplexity Thinking Machines xAI Xiaomi Z.ai

Providers

Alibaba Anthropic Cerebras Cohere DeepSeek Google Groq Meta MiniMax Mistral Moonshot AI NVIDIA OpenAI Perplexity Thinking Machines xAI Xiaomi Z.ai
NVIDIA 4 params

NVIDIA USDCode Llama 3.1 70B Instruct API parameters

These are the API parameters modelparams.dev tracks for NVIDIA USDCode Llama 3.1 70B Instruct — the settings you send in a request. Each row gives the type, default, valid range or values, and the conditions that gate it. It's the same data the JSON API serves.

After the parameter count instead — how many weights USDCode Llama 3.1 70B Instruct has? That's a different number, and we don't track it. Here's the difference.

Length 1 param
Parameter Type Default Description Condition
Max tokens
max_tokens 		integer (1…2048) 1024 Maximum number of tokens to generate. Generation stops when this limit is reached. —
Sampling 2 params
Parameter Type Default Description Condition
Temperature
temperature 		number (0…1) 0.1 Controls randomness. Lower values make outputs more focused; higher values make them more varied. Not recommended to modify both temperature and top_p in the same call. —
Top P
top_p 		number (-∞…1) 1 Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability. Not recommended to modify both temperature and top_p in the same call. —
Metadata 1 param
Parameter Type Default Description Condition
Expert type
expert_type 		enum (auto | code | knowledge | helperfunction) "auto" The type of expert to use. 'knowledge' answers with USD knowledge, 'code' responds with vanilla OpenUSD code, 'helperfunction' uses high-level helper functions, and 'auto' lets the LLM determine which expert to use. —

NVIDIA USDCode Llama 3.1 70B Instruct API parameters in brief

NVIDIA USDCode Llama 3.1 70B Instruct documents 4 API parameters, grouped by what they control:

Frequently asked questions

Which API parameters does NVIDIA USDCode Llama 3.1 70B Instruct support?
NVIDIA USDCode Llama 3.1 70B Instruct accepts 4 API parameters in the request body: temperature, top_p, max_tokens, expert_type.
What is the default temperature for NVIDIA USDCode Llama 3.1 70B Instruct?
The default temperature for NVIDIA USDCode Llama 3.1 70B Instruct is 0.1, within a valid range of 0 to 1.
What is the default top_p for NVIDIA USDCode Llama 3.1 70B Instruct?
The default top_p for NVIDIA USDCode Llama 3.1 70B Instruct is 1, with a maximum of 1.
What is the default max_tokens for NVIDIA USDCode Llama 3.1 70B Instruct?
The default max_tokens for NVIDIA USDCode Llama 3.1 70B Instruct is 1024, within a valid range of 1 to 2048.

Resources

All NVIDIA models Glossary Full catalog

USDCode Llama 3.1 70B Instruct — JSON

The full model definition as served by the API. Copy it or open the endpoint directly.

/api/v1/models/nvidia/usdcode-llama-3.1-70b-instruct.json 
{
  "$schema": "https://modelparams.dev/api/v1/schema.json",
  "provider": "nvidia",
  "authType": "api_key",
  "model": "usdcode-llama-3.1-70b-instruct",
  "params": [
    {
      "path": "temperature",
      "label": "Temperature",
      "description": "Controls randomness. Lower values make outputs more focused; higher values make them more varied. Not recommended to modify both temperature and top_p in the same call.",
      "group": "sampling",
      "type": "number",
      "default": 0.1,
      "range": {
        "min": 0,
        "max": 1
      }
    },
    {
      "path": "top_p",
      "label": "Top P",
      "description": "Controls nucleus sampling by limiting generation to tokens within the selected cumulative probability. Not recommended to modify both temperature and top_p in the same call.",
      "group": "sampling",
      "type": "number",
      "default": 1,
      "range": {
        "max": 1
      }
    },
    {
      "path": "max_tokens",
      "label": "Max tokens",
      "description": "Maximum number of tokens to generate. Generation stops when this limit is reached.",
      "group": "generation_length",
      "type": "integer",
      "default": 1024,
      "range": {
        "min": 1,
        "max": 2048
      }
    },
    {
      "path": "expert_type",
      "label": "Expert type",
      "description": "The type of expert to use. 'knowledge' answers with USD knowledge, 'code' responds with vanilla OpenUSD code, 'helperfunction' uses high-level helper functions, and 'auto' lets the LLM determine which expert to use.",
      "group": "provider_metadata",
      "type": "enum",
      "default": "auto",
      "values": [
        "auto",
        "code",
        "knowledge",
        "helperfunction"
      ]
    }
  ]
}

Other NVIDIA models

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How to use

Building with an AI agent? Hit Copy to grab this whole guide as Markdown and paste it in — or point your agent straight at /llms.txt.

modelparams.dev is an open, community-maintained catalog of model parameters. Each entry shows the knobs you can turn — type, default, range, and the conditions that gate it.

The same model accessed via an API key and via a subscription usually exposes a different set of parameters. We list both as separate entries so the data stays honest.

Catalog API

The full catalog is static JSON, CORS-enabled, served from the edge.

curl https://modelparams.dev/api/v1/models.json

Each entry is keyed by provider/model for API-key variants; subscription variants append -subscription.

If you only need the params for one model contract, use the providerless endpoint. Subscription contracts are model slugs with -subscription. 

curl https://modelparams.dev/api/v1/params/gpt-5.5.json
curl https://modelparams.dev/api/v1/params/gpt-5.5-subscription.json

Single model

curl https://modelparams.dev/api/v1/models/anthropic/claude-opus-4-7.json
curl https://modelparams.dev/api/v1/models/anthropic/claude-opus-4-7-subscription.json

JSON Schema

Every entry validates against a JSON Schema you can use in your editor or pipeline. 

curl https://modelparams.dev/api/v1/schema.json

Add this header to any YAML you author for autocomplete in VS Code: 

# yaml-language-server: $schema=https://modelparams.dev/api/v1/schema.json

Logos

Provider logos are available at /assets/logos/{provider}.svg where {provider} is the provider slug. They use currentColor so they inherit your text color. 

curl https://modelparams.dev/assets/logos/anthropic.svg

Logos are sourced from the models.dev repo (MIT) and used under nominative fair use.

Contribute

The data lives in YAML under models/{provider}/{model}-{auth}.yaml in the GitHub repo. Open a PR; CI validates against the schema and rebuilds.

Edit on GitHub MIT licensed