semvec/pypi-packages huggingface dataset:
ovos-solver-alpacacpp-plugin 0.0.0a1
A question solver plugin for ovos
License: MIT
Keywords: ovos plugin utterance fallback query

# <img src='' card_color='#40DBB0' width='50' height='50' style='vertical-align:bottom'/> AlpacaPP Persona
 
Give OpenVoiceOS some sass with [AlpacaCPP](https://github.com/antimatter15/alpaca.cpp)

## Examples 
* "What is best in life?"
* "Do you like dogs"
* "Does God exist?"


## Usage

Spoken answers api

```python
from ovos_solver_alpacacpp import AlpacaCPPSolver

ALPACA_MODEL_FILE = "/./models/ggml-alpaca-7b-q4.bin"

bot = AlpacaCPPSolver({"model": ALPACA_MODEL_FILE})

sentence = bot.spoken_answer("Qual é o teu animal favorito?", {"lang": "pt-pt"})
# Meus animais favoritos são cães, gatos e tartarugas!

for q in ["Does god exist?",
          "what is the speed of light?",
          "what is the meaning of life?",
          "What is your favorite color?",
          "What is best in life?"]:
    a = bot.get_spoken_answer(q)
    print(q, a)
```


## Credit

This combines [Facebook's LLaMA](https://github.com/facebookresearch/llama), [Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [alpaca-lora](https://github.com/tloen/alpaca-lora) and [corresponding weights](https://huggingface.co/tloen/alpaca-lora-7b/tree/main) by Eric Wang (which uses [Jason Phang's implementation of LLaMA](https://github.com/huggingface/transformers/pull/21955) on top of Hugging Face Transformers), and [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov. The chat implementation is based on Matvey Soloviev's [Interactive Mode](https://github.com/ggerganov/llama.cpp/pull/61) for llama.cpp. Inspired by [Simon Willison's](https://til.simonwillison.net/llms/llama-7b-m2) getting started guide for LLaMA. [Andy Matuschak](https://twitter.com/andy_matuschak/status/1636769182066053120)'s thread on adapting this to 13B, using fine tuning weights by [Sam Witteveen](https://huggingface.co/samwit/alpaca13B-lora). 


## Disclaimer

Note that the model weights are only to be used for research purposes, as they are derivative of LLaMA, and uses the published instruction data from the Stanford Alpaca project which is generated by OpenAI, which itself disallows the usage of its outputs to train competing models.
ovos-solver-llamacpp-plugin 0.0.0a1
A question solver plugin for ovos
License: MIT
Keywords: ovos plugin utterance fallback query

# <img src='https://camo.githubusercontent.com/57d5fd32c5b51e73fce9077a45f155db3edecd5dfe31d272d73569cb23ef779c/68747470733a2f2f692e696d6775722e636f6d2f6c41645a6a376d2e6a706567' card_color='#40DBB0' width='50' height='50' style='vertical-align:bottom'/> LlamaCPP Persona
 
Give OpenVoiceOS some sass with [LLaMA](https://arxiv.org/abs/2302.13971) model in [pure C/C++](https://github.com/ggerganov/llama.cpp)

## Examples 
* "What is best in life?"
* "Do you like dogs"
* "Does God exist?"


## Usage

Spoken answers api

```python
from ovos_solver_llamacpp import LlamaCPPSolver

LLAMA_MODEL_FILE = "./models/ggml-model-q4_0.bin"

# persona = "omniscient oracle" # hardcoded personas, "explainer"|"bob"|"omniscient oracle"
persona = "helpful, kind, honest, good at writing"  # description of assistant
bot = LlamaCPPSolver({"model": LLAMA_MODEL_FILE, 
                      "persona": persona})

sentence = bot.spoken_answer("Qual é o teu animal favorito?", {"lang": "pt-pt"})
# Meus animais favoritos são cães, gatos e tartarugas!

for q in ["Does god exist?",
          "what is the speed of light?",
          "what is the meaning of life?",
          "What is your favorite color?",
          "What is best in life?"]:
    a = bot.get_spoken_answer(q)
    print(q, a)
```