Meta Llama 3.1 (70B)

The meta.llama-3.1-70b-instruct model is available for on-demand inferencing, dedicated hosting, and fine-tuning, and is perfect for content creation, conversational AI, and enterprise applications. Its key strengths include:

Summarizing, rewording, and classifying text with high accuracy
Sentiment analysis and language modeling capabilities
Effective dialogue systems
Code generation

Available in These Regions

Brazil East (Sao Paulo)
Germany Central (Frankfurt)
Japan Central (Osaka)
UK South (London)
US Midwest (Chicago)

Key Features

Model Size: 70 billion parameters
Context Length: 128,000 tokens, which is 16 times increase from the Meta Llama 3 models. (Maximum prompt + response length: 128,000 tokens for each run.)
Multilingual Support: English, French, German, Hindi, Italian, Portuguese, Spanish, and Thai
For on-demand inferencing, the response length is capped at 4,000 tokens for each run.
You can fine-tune this model with your dataset.

Dedicated AI Cluster for the Model

To reach a model through a dedicated AI cluster in any listed region, you must create an endpoint for that model on a dedicated AI cluster. For the cluster unit size that matches this model, see the following table.


Base Model	Fine-Tuning Cluster	Hosting Cluster	Pricing Page Information	Request Cluster Limit Increase
Model Name: Meta Llama 3.1 (70B) OCI Model Name: `meta.llama-3.1-70b-instruct`	Unit Size: Large Generic Required Units: 2	Unit Size: Large Generic Required Units: 1	Pricing Page Product Name: Large Meta - Dedicated For Hosting, Multiply the Unit Price: x2 For Fine-Tuning, Multiply the Unit Price: x4	Limit Name: `dedicated-unit-llama2-70-count` For Hosting, Request Limit Increase by: 2 For Fine-Tuning, Request Limit Increase by: 4

Tip

If you don't have enough cluster limits in your tenancy for hosting the Meta Llama 3.1 (70B) model on a dedicated AI cluster, request the dedicated-unit-llama2-70-count limit to increase by 2.
For fine-tuning, request the dedicated-unit-llama2-70-count limit to increase by 4.
Review the Meta Llama 3.1 (70B) cluster performance benchmarks for different use cases.

Release and Retirement Dates


Model	Release Date	On-Demand Retirement Date	Dedicated Mode Retirement Date
`meta.llama-3.1-70b-instruct`	2024-09-19	`2025-06-17`	`2025-08-07`

Important

For a list of all model time lines and retirement details, see Retiring the Models.

Model Parameters

To change the model responses, you can change the values of the following parameters in the playground or the API.

Maximum output tokens

The maximum number of tokens that you want the model to generate for each response. Estimate four characters per token. Because you're prompting a chat model, the response depends on the prompt and each response doesn't necessarily use up the maximum allocated tokens.

Temperature

The level of randomness used to generate the output text.

Tip

Start with the temperature set to 0 or less than one, and increase the temperature as you regenerate the prompts for a more creative output. High temperatures can introduce hallucinations and factually incorrect information.

Top p

A sampling method that controls the cumulative probability of the top tokens to consider for the next token. Assign p a decimal number between 0 and 1 for the probability. For example, enter 0.75 for the top 75 percent to be considered. Set p to 1 to consider all tokens.

Top k

A sampling method in which the model chooses the next token randomly from the top k most likely tokens. A high value for k generates more random output, which makes the output text sound more natural. The default value for k is 0 for Cohere Command models and -1 for Meta Llama models, which means that the model should consider all tokens and not use this method.

Frequency penalty

A penalty that's assigned to a token when that token appears frequently. High penalties encourage fewer repeated tokens and produce a more random output.

For the Meta Llama family models, this penalty can be positive or negative. Positive numbers encourage the model to use new tokens and negative numbers encourage the model to repeat the tokens. Set to 0 to disable.

Presence penalty

A penalty that's assigned to each token when it appears in the output to encourage generating outputs with tokens that haven't been used.

Seed

A parameter that makes a best effort to sample tokens deterministically. When this parameter is assigned a value, the large language model aims to return the same result for repeated requests when you assign the same seed and parameters for the requests.

Allowed values are integers and assigning a large or a small seed value doesn't affect the result. Assigning a number for the seed parameter is similar to tagging the request with a number. The large language model aims to generate the same set of tokens for the same integer in consecutive requests. This feature is especially useful for debugging and testing. The seed parameter has no maximum value for the API, and in the Console, its maximum value is 9999. Leaving the seed value blank in the Console, or null in the API disables this feature.

Warning

The seed parameter might not produce the same result in the long-run, because the model updates in the OCI Generative AI service might invalidate the seed.