Instructions to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix", filename="L3-TheSpice-8b-v0.1.3-F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
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 Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
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 Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix 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 Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix 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 Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.L3-TheSpice-8b-v0.1.3-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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# #roleplay
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GGUF-IQ-Imatrix quants for **cgato/L3-TheSpice-8b-v0.
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> [!IMPORTANT]
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> These quants have been done after the fixes from [llama.cpp/pull/6920](https://github.com/ggerganov/llama.cpp/pull/6920). <br>
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# Original model information by the author:
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Now not overtrained and with the tokenizer fix to base llama3. Trained for 3 epochs.
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The latest TheSpice, dipped in Mama Liz's LimaRP Oil.
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I've focused on making the model more flexible and provide a more unique experience.
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I'm still working on cleaning up my dataset, but I've shrunken it down a lot to focus on a "less is more" approach.
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This is ultimate a return to form of the way I used to train Thespis, with more of a focus on a small hand edited dataset.
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## Datasets Used
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* Capybara
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* Claude Multiround 30k
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* Augmental
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* ToxicQA
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* Yahoo Answers
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* Airoboros 3.1
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* LimaRP
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## Features ( Examples from 0.1.1 because I'm too lazy to take new screenshots. Its tested tho. )
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Narration
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If you request information on objects or characters in the scene, the model will narrate it to you. Most of the time, without moving the story forward.
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# You can look at anything mostly as long as you end it with "What do I see?"
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# You can also request to know what a character is thinking or planning.
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# You can ask for a quick summary on the character as well.
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# Before continuing the conversation as normal.
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## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )
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If you're using Ooba in verbose mode as a server, you can check if you're console is logging something that looks like this.
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```
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{System Prompt}
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Username: {Input}
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BotName: {Response}
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Username: {Input}
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BotName: {Response}
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```
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## Presets
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All screenshots above were taken with the below SillyTavern Preset.
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## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.05)
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This is a roughly equivalent Kobold Horde Preset.
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## Recommended Kobold Horde Preset -> MinP
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# Disclaimer
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Please prompt responsibly and take anything outputted by any Language Model with a huge grain of salt. Thanks!
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# #roleplay
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GGUF-IQ-Imatrix quants for **cgato/L3-TheSpice-8b-v0.1.3**.
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> [!IMPORTANT]
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> These quants have been done after the fixes from [llama.cpp/pull/6920](https://github.com/ggerganov/llama.cpp/pull/6920). <br>
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