vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into compilade/pyright-tests

Browse source
This commit is contained in:
Francis Couture-Harpin 2024-07-07 10:51:30 -04:00
commit 0caf60a79e
31 changed files with 9399 additions and 73 deletions
Download patch file Download diff file Expand all files Collapse all files

4
examples/server/README.md
View file

@ -366,7 +366,8 @@ Notice that each `probs` is an array of length `n_probs`.
"assistant_name": "",
"user_name": "",
"default_generation_settings": { ... },
"total_slots": 1
"total_slots": 1,
"chat_template": ""
}
```
@ -374,6 +375,7 @@ Notice that each `probs` is an array of length `n_probs`.
- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- `chat_template` - the model's original Jinja2 prompt template
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.

13
examples/server/server.cpp
View file

@ -2605,7 +2605,7 @@ int main(int argc, char ** argv) {
// if a custom chat template is not supplied, we will use the one that comes with the model (if any)
if (params.chat_template.empty()) {
if (!ctx_server.validate_model_chat_template()) {
LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
LOG_WARNING("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
params.chat_template = "chatml";
}
}
@ -2967,11 +2967,20 @@ int main(int argc, char ** argv) {
};
const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
std::string template_key = "tokenizer.chat_template", curr_tmpl;
int32_t tlen = llama_model_meta_val_str(ctx_server.model, template_key.c_str(), nullptr, 0);
if (tlen > 0) {
std::vector<char> curr_tmpl_buf(tlen + 1, 0);
if (llama_model_meta_val_str(ctx_server.model, template_key.c_str(), curr_tmpl_buf.data(), curr_tmpl_buf.size()) == tlen) {
curr_tmpl = std::string(curr_tmpl_buf.data(), tlen);
}
}
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
{ "system_prompt", ctx_server.system_prompt.c_str() },
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
{ "total_slots", ctx_server.params.n_parallel }
{ "total_slots", ctx_server.params.n_parallel },
{ "chat_template", curr_tmpl.c_str() }
};
res.set_content(data.dump(), "application/json; charset=utf-8");