ovos-solver-gguf-plugin 0.0.0a4

A question solver plugin for OVOS

GGUF Solver

Overview

GGUFSolver is a question-answering module that utilizes GGUF models to provide responses to user queries. This solver streams utterances for real-time interaction and is built on the ovos_plugin_manager.templates.solvers.QuestionSolver framework.

Features

• Supports loading GGUF models from local files or remote repositories.
• Streams partial responses for improved interactivity.
• Configurable persona and verbosity settings.
• Capable of providing spoken answers.

Configuration

GGUFSolver requires a configuration dictionary. The configuration should at least specify the model to use. Here is an example configuration:

cfg = {
    "model": "TheBloke/notus-7B-v1-GGUF",
    "remote_filename": "*Q4_K_M.gguf"
}

• model: The identifier for the model. It can be a local file path or a repository ID for a remote model.
• n_gpu_layers: how many layer to offload to GPU, -1 to offload all. default 0
• remote_filename: The specific filename to load from a remote repository.
• chat_format: (Optional) Chat formatting settings.
• verbose: (Optional) Set to True for detailed logging.
• persona: (Optional) Persona for the system messages. Default is "You are a helpful assistant who gives short factual answers".
• max_tokens: (Optional) Maximum tokens for the response. Default is 512.

NOTE: for GPU support llama.cpp needs to be compiled with

CMAKE_ARGS="-DGGML_CUDA=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --no-cache-dir

Usage

Initializing the Solver

from ovos_gguf_solver import GGUFSolver
from ovos_utils.log import LOG

LOG.set_level("DEBUG")

cfg = {
    "model": "TheBloke/notus-7B-v1-GGUF",
    "remote_filename": "*Q4_K_M.gguf"
}

solver = GGUFSolver(cfg)

Streaming Utterances

Use the stream_utterances method to stream responses. This is particularly useful for real-time applications such as voice assistants.

query = "tell me a joke about aliens"
for sentence in solver.stream_utterances(query):
    print(sentence)

Getting a Full Answer

Use the get_spoken_answer method to get a complete response.

query = "What is the capital of France?"
answer = solver.get_spoken_answer(query)
print(answer)

Integrating with Persona Framework

To integrate GGUFSolver with the OVOS Persona Framework and pass solver configurations, follow these examples.

Each example demonstrates how to define a persona configuration file with specific settings for different models or configurations.

To use any of these configurations, run the OVOS Persona Server with the desired configuration file:

$ ovos-persona-server --persona gguf_persona_remote.json

Replace gguf_persona_remote.json with the filename of the configuration you wish to use.

Example 1: Using a Remote GGUF Model

This example shows how to configure the GGUFSolver to use a remote GGUF model with a specific persona.

gguf_persona_remote.json:

{
  "name": "Notus",
  "solvers": [
    "ovos-solver-gguf-plugin"
  ],
  "ovos-solver-gguf-plugin": {
    "model": "TheBloke/notus-7B-v1-GGUF",
    "remote_filename": "*Q4_K_M.gguf",
    "persona": "You are an advanced assistant providing detailed and accurate information.",
    "verbose": true
  }
}

In this configuration:

• ovos-solver-gguf-plugin is set to use a remote GGUF model TheBloke/notus-7B-v1-GGUF with the specified filename.
• The persona is configured to provide detailed and accurate information.
• verbose is set to true for detailed logging.

Example 2: Using a Local GGUF Model

This example shows how to configure the GGUFSolver to use a local GGUF model.

gguf_persona_local.json:

{
  "name": "LocalGGUFPersona",
  "solvers": [
    "ovos-solver-gguf-plugin"
  ],
  "ovos-solver-gguf-plugin": {
    "model": "/path/to/local/model/gguf_model.gguf",
    "persona": "You are a helpful assistant providing concise answers.",
    "max_tokens": 256
  }
}

In this configuration:

• ovos-solver-gguf-plugin is set to use a local GGUF model located at /path/to/local/model/gguf_model.gguf.
• The persona is configured to provide concise answers.
• max_tokens is set to 256 to limit the response length.

Example Models

these models are not endorsed and this list was largely compiling by searching hugging face, only for illustrative purposes

Language	Model Name	URL	Description
English	CausalLM-14B-GGUF	Link	A 14B parameter model compatible with Meta LLaMA 2, demonstrating top-tier performance among models with fewer than 70B parameters, optimized for both qualitative and quantitative evaluations, with strong consistency across versions.
English	Phi-3-Mini-4K-Instruct-GGUF	Link	A lightweight 3.8B parameter model from the Phi-3 family, optimized for strong reasoning and long-context tasks with robust performance in instruction adherence and logical reasoning.
English	Qwen2-0.5B-Instruct-GGUF	Link	A 0.5B parameter instruction-tuned model from the Qwen2 series, excelling in language understanding, generation, and multilingual tasks with competitive performance against state-of-the-art models.
English	GritLM_-_GritLM-7B-gguf	Link	A unified model for both text generation and embedding tasks, achieving state-of-the-art performance in both areas and enhancing Retrieval-Augmented Generation (RAG) efficiency by over 60%.
English	falcon-7b-instruct-GGUF	Link	A 7B parameter instruct model based on Falcon-7B, optimized for chat and instruction tasks with performance benefits from extensive training on 1,500B tokens, and optimized inference architecture.
English	Samantha-Qwen-2-7B-GGUF	Link	A quantized 7B parameter model fine-tuned with QLoRa and FSDP, tailored for conversational tasks and utilizing datasets like OpenHermes-2.5 and Opus_Samantha.
English	Mistral-7B-Instruct-v0.3-GGUF	Link	An instruct fine-tuned model based on Mistral-7B-v0.3, featuring an extended vocabulary and support for function calling, aimed at demonstrating effective fine-tuning with room for improved moderation mechanisms.
English	Lite-Mistral-150M-v2-Instruct-GGUF	Link	A compact 150M parameter model optimized for efficiency on various devices, demonstrating reasonable performance in simple queries but facing challenges with context preservation and accuracy in multi-turn conversations.
English	TowerInstruct-7B-v0.1-GGUF	Link	A 7B parameter model fine-tuned on the TowerBlocks dataset for translation tasks, including general, context-aware, and terminology-aware translation, as well as named-entity recognition and grammatical error correction.
English	Dr_Samantha-7B-GGUF	Link	A merged model incorporating medical and psychological knowledge, with extensive performance on medical knowledge tasks and a focus on whole-person care.
English	phi-2-orange-GGUF	Link	A finetuned model based on Phi-2, optimized with a two-step finetuning approach for improved performance in various evaluation metrics. The model is designed for Python-related tasks and general question answering.
English	phi-2-electrical-engineering-GGUF	Link	The phi-2-electrical-engineering model excels in answering questions and generating code specifically for electrical engineering and Kicad software, boasting efficient deployment and a focus on technical accuracy within its 2.7 billion parameters.
English	Unholy-v2-13B-GGUF	Link	An uncensored 13B parameter model merged with various models for an uncensored experience, designed to bypass typical content moderation filters.
English	CapybaraHermes-2.5-Mistral-7B-GGUF	Link	A preference-tuned 7B model using distilabel, optimized for multi-turn performance with improved scores in benchmarks like MTBench and Nous, compared to the Mistral-7B-Instruct-v0.2.
English	notus-7B-v1-GGUF	Link	A 7B parameter model fine-tuned with Direct Preference Optimization (DPO), surpassing Zephyr-7B-beta and Claude 2 on AlpacaEval, designed for chat-like applications with improved preference-based performance.
English	Luna AI Llama2 Uncensored GGML	Link	A Llama2-based chat model fine-tuned on over 40,000 long-form chat discussions. Optimized with synthetic outputs, available in both 4-bit GPTQ for GPU and GGML for CPU inference. Prompt format follows Vicuna 1.1/OpenChat style.
English	Zephyr-7B-β-GGUF	Link	A 7B parameter model fine-tuned with Direct Preference Optimization (DPO) to enhance performance, optimized for helpfulness but may generate problematic text due to removed in-built alignment.
English	TinyLlama-1.1B-1T-OpenOrca-GGUF	Link	A 1.1B parameter model fine-tuned on the OpenOrca GPT-4 subset, optimized for conversational tasks with a focus on efficiency and performance in the CHATML format.
English	LlongOrca-7B-16K-GGUF	Link	A fine-tuned 7B parameter model optimized for long contexts, achieving top performance in long-context benchmarks and notable improvements over the base model, with efficient training using OpenChat's MultiPack algorithm.
English	Meta-Llama-3-8B-Instruct-GGUF	Link	An 8B parameter instruction-tuned model from the Llama 3 series, optimized for dialogue and outperforming many open-source models on industry benchmarks, with a focus on helpfulness and safety through advanced fine-tuning techniques.
English	Smol-7B-GGUF	Link	A fine-tuned 7B parameter model from the Smol series, known for its strong performance in diverse NLP tasks and efficient fine-tuning techniques.
English	Smol-Llama-101M-Chat-v1-GGUF	Link	A compact 101M parameter chat model optimized for diverse conversational tasks, showing balanced performance across multiple benchmarks with a focus on efficiency and low-resource scenarios.
English	Sonya-7B-GGUF	Link	A high-performing 7B model with excellent scores in MT-Bench, ideal for various tasks including assistant and roleplay, combining multiple sources to achieve superior performance.
English	WizardLM-7B-uncensored-GGML	Link	An uncensored 7B parameter model from the WizardLM series, designed without built-in alignment to allow for custom alignment via techniques like RLHF LoRA, with no guardrails and complete responsibility for content usage resting on the user.
English	OpenChat 3.5	Link	A 7B parameter model that achieves comparable results with ChatGPT, excelling in MT-bench evaluations. It utilizes mixed-quality data and C-RLFT (a variant of offline reinforcement learning) for training. OpenChat 3.5 performs well across various benchmarks and has been optimized for high-throughput deployment. It is an open-source model with strong performance in chat-based applications.
Portuguese	PORTULAN_-_gervasio-7b-portuguese-ptpt-decoder-gguf	Link	Gervásio 7B PTPT is an open decoder for Portuguese, built on the LLaMA-2 7B model, fine-tuned with instruction data to excel in various Portuguese tasks, and designed to run on consumer-grade hardware with a focus on European Portuguese.
Portuguese	CabraLlama3-8b-GGUF	Link	A refined version of Meta-Llama-3-8B-Instruct, optimized with the Cabra 30k dataset for understanding and responding in Portuguese, providing enhanced performance for Portuguese language tasks.
Portuguese	bode-7b-alpaca-pt-br-gguf	Link	Bode-7B is a fine-tuned LLaMA 2-based model designed for Portuguese, delivering satisfactory results in classification tasks and prompt-based applications.
Portuguese	bode-13b-alpaca-pt-br-gguf	Link	Bode-13B is a fine-tuned LLaMA 2-based model for Portuguese prompts, offering enhanced performance over its 7B counterpart, and designed for both research and commercial applications with a focus on Portuguese language tasks.
Portuguese	sabia-7B-GGUF	Link	Sabiá-7B is a Portuguese auto-regressive language model based on LLaMA-1-7B, pretrained on a large Portuguese dataset, offering high performance in few-shot tasks and generating text, with research-only licensing.
Portuguese	OpenHermesV2-PTBR-portuguese-brazil-gguf	Link	A finetuned version of Mistral 7B trained on diverse GPT-4 generated data, designed for Portuguese, with extensive filtering and transformation for enhanced performance.
Catalan	CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF	Link	An instruction-tuned model optimized with DPO for various NLP tasks in Catalan, including translation, NER, summarization, and sentiment analysis, built on an auto-regressive transformer architecture.

The models listed are suggestions. The best model for your use case will depend on your specific requirements such as language, task complexity, and performance needs.