Instructions to use SerialKicked/Lethe-AI-Repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SerialKicked/Lethe-AI-Repo with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SerialKicked/Lethe-AI-Repo", filename="emotion-bert-classifier.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use SerialKicked/Lethe-AI-Repo with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SerialKicked/Lethe-AI-Repo:Q6_K # Run inference directly in the terminal: llama-cli -hf SerialKicked/Lethe-AI-Repo:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SerialKicked/Lethe-AI-Repo:Q6_K # Run inference directly in the terminal: llama-cli -hf SerialKicked/Lethe-AI-Repo:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf SerialKicked/Lethe-AI-Repo:Q6_K # Run inference directly in the terminal: ./llama-cli -hf SerialKicked/Lethe-AI-Repo:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf SerialKicked/Lethe-AI-Repo:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf SerialKicked/Lethe-AI-Repo:Q6_K
Use Docker
docker model run hf.co/SerialKicked/Lethe-AI-Repo:Q6_K
- LM Studio
- Jan
- Ollama
How to use SerialKicked/Lethe-AI-Repo with Ollama:
ollama run hf.co/SerialKicked/Lethe-AI-Repo:Q6_K
- Unsloth Studio new
How to use SerialKicked/Lethe-AI-Repo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SerialKicked/Lethe-AI-Repo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SerialKicked/Lethe-AI-Repo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SerialKicked/Lethe-AI-Repo to start chatting
- Docker Model Runner
How to use SerialKicked/Lethe-AI-Repo with Docker Model Runner:
docker model run hf.co/SerialKicked/Lethe-AI-Repo:Q6_K
- Lemonade
How to use SerialKicked/Lethe-AI-Repo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SerialKicked/Lethe-AI-Repo:Q6_K
Run and chat with the model
lemonade run user.Lethe-AI-Repo-Q6_K
List all available models
lemonade list
Update README.md
Browse files
README.md
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license: mit
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This repo contains useful content for the [Lethe AI Sharp](https://github.com/SerialKicked/Lethe-AI-Sharp/) library
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It unifies: chat personas, conversation/session management, streaming inference, long‑term memory, RAG (retrieval augmented generation), background agentic tasks, web search tools, TTS, and structured output generation. It is extensible, documented, and backend-agnostic (you write the same code no matter which backend is being used)
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**Self-Contained:** Built-in LlamaSharp backend means you can distribute a single executable that runs LLMs locally. No external server required, but external servers (KoboldAPI and OpenAI API) are supported too.
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This repo also contains fixed Jinja templates for Qwen 3.5 models. This one allows for system messages mid conversations (requirement for LetheAI) while removing an error that would trigger (at least) on LM Studio.
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A Q6_K quantized version of [General Text Embeddings (GTE) model](https://huggingface.co/thenlper/gte-large) under MIT License. Used for all things RAG in the library.
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A quantized version of [Emotions Analyzer](https://huggingface.co/logasanjeev/emotions-analyzer-bert) under MIT License, a fine-tuned BERT-base-uncased on GoEmotions for multi-label classification (28 emotions). Used for experimental sentiment analysis tasks.
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license: mit
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# Repo Information
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This repo contains useful content for the [Lethe AI Sharp](https://github.com/SerialKicked/Lethe-AI-Sharp/) library: classification models, fixed jinja templates, and other files. More information about the library below.
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## Fixed Jinja Templates
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This repo also contains fixed (and more permissive) Jinja templates for Mistral (Tekken7) and Qwen 3.5 (ChatML) models. They allow for system messages mid conversations (requirement for LetheAI) while other so-called errors that could trigger when using those LLM in way not initially intended.
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## gte-large.Q6_K.gguf
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A Q6_K quantized version of [General Text Embeddings (GTE) model](https://huggingface.co/thenlper/gte-large) under MIT License. Used for all things RAG in the library.
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## emotion-bert-classifier.gguf
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A quantized version of [Emotions Analyzer](https://huggingface.co/logasanjeev/emotions-analyzer-bert) under MIT License, a fine-tuned BERT-base-uncased on GoEmotions for multi-label classification (28 emotions). Used for experimental sentiment analysis tasks. Optional.
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# What is Lethe AI Sharp?
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[Lethe AI Sharp](https://github.com/SerialKicked/Lethe-AI-Sharp/) is a modular, object‑oriented C# library that connects local or remote Large Language Model (LLM) backends to your applications (desktop tools, game engines, services). It also comes with its own light backend, allowing you to run a local LLM in the GGUF format directly without even having to rely on anything else.
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It unifies: chat personas, conversation/session management, streaming inference, long‑term memory, RAG (retrieval augmented generation), background agentic tasks, web search tools, TTS, and structured output generation. It is extensible, documented, and backend-agnostic (you write the same code no matter which backend is being used)
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**Self-Contained:** Built-in LlamaSharp backend means you can distribute a single executable that runs LLMs locally. No external server required, but external servers (KoboldAPI and OpenAI API) are supported too.
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# Any program using Lethe AI Sharp?
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Sure there's [Lethe AI Chat](https://github.com/SerialKicked/Lethe-AI-Chat/), it's a native Windows 10 frontend chat application. Compiled binaries are [available here](https://github.com/SerialKicked/Lethe-AI-Chat/releases).
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It's (at the very least) on par with other modern chat programs, and contains many unique features.
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