Meta: Llama 3.1 8B Instruct

Meta: Llama 3.1 8B Instruct is an instruction-tuned text model from Meta, part of the Llama 3.1 family that launched in July 2024. The 8B size sits at the smaller end of that family, which makes it a reasonable default when you want a capable general purpose assistant without the weight of a much larger model. It is a text-only model, so it works well for chat, drafting, extraction and similar language tasks rather than anything involving images or audio.

The model takes text in and returns text out. It supports a context window of 131,072 tokens, which is large enough to hold long documents, extended conversations or sizeable code files in a single request. The maximum output is 117,964 tokens, so long generations are possible within that window. Its knowledge cutoff is 2023-12-31, meaning events and information after that date are not part of its training data. Where accuracy depends on recent facts, you should supply the relevant context yourself rather than rely on the model's memory.

Those characteristics point to a few practical uses. Summarising and question answering over long inputs fit the large context window well. Instruction following makes it suitable for chat assistants, rewriting, classification and structured data extraction from messy text. Because it accepts tools and structured outputs, it can also serve as the reasoning step in a pipeline that calls functions or returns JSON. The 8B size keeps latency and resource use comparatively low, which helps when you are running many requests or prototyping an idea before committing to a larger model.

You can call it through api14's OpenAI-compatible API using the model id meta-llama/llama-3.1-8b-instruct. Point your existing OpenAI client at the api14 base URL and pass that id in the model field. The model accepts frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs and top_p. That covers the usual sampling controls plus tool calling, structured outputs and reproducible runs via seed.

One thing to weigh is the knowledge cutoff and the model's size. If your task needs facts from after 2023-12-31, plan to provide them in the prompt or through retrieval. If you need image or audio input, this model does not accept it, and a multimodal model would be a better fit. For the hardest reasoning or the most demanding generation quality, a larger model in the same family may suit you better, while this 8B version remains a solid choice for high volume, lower complexity work.