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docs(models-http-api): add completion support for llamafile

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Wei Zhang 2024-11-13 00:34:52 +08:00
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30
website/docs/references/models-http-api/llamafile.md vendored
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@ -3,15 +3,16 @@
[llamafile](https://github.com/Mozilla-Ocho/llamafile)
is a Mozilla Builders project that allows you to distribute and run LLMs with a single file.
llamafile provides an OpenAI API-compatible chat-completions and embedding endpoint,
enabling us to use the OpenAI kinds for chat and embeddings.
llamafile embeds a llama.cpp server and provides an OpenAI API-compatible chat-completions endpoint,
allowing us to use the `openai/chat`, `llama.cpp/completion`, and `llama.cpp/embedding` types.
However, for completion, there are certain differences in the implementation, and we are still working on it.
llamafile uses port `8080` by default, which is also the port used by Tabby.
By default, llamafile uses port `8080`, which is also used by Tabby.
Therefore, it is recommended to run llamafile with the `--port` option to serve on a different port, such as `8081`.
Below is an example for chat:
For embeddings, the embedding endpoint is no longer supported in the standard llamafile server,
so you need to run llamafile with the `--embedding` and `--port` options.
Below is an example configuration:
```toml title="~/.tabby/config.toml"
# Chat model
@ -20,16 +21,19 @@ kind = "openai/chat"
model_name = "your_model"
api_endpoint = "http://localhost:8081/v1"
api_key = ""
```
For embeddings, the embedding endpoint is no longer supported in the standard llamafile server,
so you have to run llamafile with the `--embedding` option and set the Tabby config to:
# Completion model
[model.completion.http]
kind = "llama.cpp/completion"
model_name = "your_model"
api_endpoint = "http://localhost:8081"
api_key = "secret-api-key"
prompt_template = "<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>" # Example prompt template for the Qwen2.5 Coder model series.
```toml title="~/.tabby/config.toml"
# Embedding model
[model.embedding.http]
kind = "openai/embedding"
kind = "llama.cpp/embedding"
model_name = "your_model"
api_endpoint = "http://localhost:8082/v1"
api_endpoint = "http://localhost:8082"
api_key = ""
```
```