Meta: Llama 3.1 70B Instruct

Meta: Llama 3.1 70B Instruct is a text generation model from Meta, part of the Llama 3.1 family that launched on 23 July 2024. It is the 70 billion parameter version, tuned for instruction following, which means it is designed to respond to prompts and carry out dialogue rather than simply continue raw text. Meta positions this instruct variant as optimised for high quality dialogue use cases, and it sits in the middle of the Llama 3.1 range in terms of size.

The model accepts text as input and returns text as output, so it is a pure language model with no image, audio or video handling. Its context window is 131,072 tokens, which is large enough to hold long documents, extended conversations or sizeable code files in a single request. The maximum output length is 16,384 tokens, so responses can be substantial but are capped well below the input limit. Its knowledge cutoff is 31 December 2023, meaning anything after that date is outside what it learned during training.

Those characteristics point to a fairly clear set of practical uses. The long context suits summarisation and question answering over lengthy documents, contract review, research synthesis and multi turn chat where earlier parts of the conversation need to stay in view. Instruction tuning makes it a reasonable fit for assistants, drafting, rewriting, classification and extraction tasks. Because the model supports tool calling and structured outputs, it can also be used in pipelines where the model needs to return data in a defined shape or invoke external functions rather than only produce free text.

You call it through api14's OpenAI-compatible API using the model id meta-llama/llama-3.1-70b-instruct. Because the interface follows the OpenAI schema, you can point an existing OpenAI client at api14's base URL and pass that id in the model field. The request parameters it supports are frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. That covers the usual sampling controls, deterministic output via seed, stop sequences, and both tool calling and structured output modes.

A few limits are worth keeping in mind when deciding. Output is capped at 16,384 tokens even though the context window is much larger, so tasks that need very long single responses may require splitting or a different model. The knowledge cutoff of 31 December 2023 means recent events are not covered unless you supply them in the prompt. And since input and output are text only, anything involving images or audio falls outside what this model can do.