Instructions to use RichardErkhov/Karko_-_Proctora-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/Karko_-_Proctora-gguf 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 RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Karko_-_Proctora-gguf: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 RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/Karko_-_Proctora-gguf: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 RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/Karko_-_Proctora-gguf with Ollama:
ollama run hf.co/RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/Karko_-_Proctora-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/Karko_-_Proctora-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/Karko_-_Proctora-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Karko_-_Proctora-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
Proctora - GGUF
- Model creator: https://huggingface.co/Karko/
- Original model: https://huggingface.co/Karko/Proctora/
| Name | Quant method | Size |
|---|---|---|
| Proctora.Q2_K.gguf | Q2_K | 4.43GB |
| Proctora.IQ3_XS.gguf | IQ3_XS | 4.95GB |
| Proctora.IQ3_S.gguf | IQ3_S | 5.22GB |
| Proctora.Q3_K_S.gguf | Q3_K_S | 5.2GB |
| Proctora.IQ3_M.gguf | IQ3_M | 5.35GB |
| Proctora.Q3_K.gguf | Q3_K | 5.78GB |
| Proctora.Q3_K_M.gguf | Q3_K_M | 5.78GB |
| Proctora.Q3_K_L.gguf | Q3_K_L | 6.27GB |
| Proctora.IQ4_XS.gguf | IQ4_XS | 6.5GB |
| Proctora.Q4_0.gguf | Q4_0 | 6.78GB |
| Proctora.IQ4_NL.gguf | IQ4_NL | 6.85GB |
| Proctora.Q4_K_S.gguf | Q4_K_S | 6.84GB |
| Proctora.Q4_K.gguf | Q4_K | 7.25GB |
| Proctora.Q4_K_M.gguf | Q4_K_M | 7.25GB |
| Proctora.Q4_1.gguf | Q4_1 | 7.52GB |
| Proctora.Q5_0.gguf | Q5_0 | 4.69GB |
| Proctora.Q5_K_S.gguf | Q5_K_S | 0.62GB |
| Proctora.Q5_K.gguf | Q5_K | 8.51GB |
| Proctora.Q5_K_M.gguf | Q5_K_M | 8.51GB |
| Proctora.Q5_1.gguf | Q5_1 | 9.01GB |
| Proctora.Q6_K.gguf | Q6_K | 9.84GB |
| Proctora.Q8_0.gguf | Q8_0 | 12.75GB |
Original model description:
license: cc-by-nc-4.0 pipeline_tag: text-generation tags: - moe - merge
Proctora is a MoE model made of
- OpenPipe/mistral-ft-optimized-1227 as a base model
- SanjiWatsuki/Kunoichi-7B as a first expert dedicated to RP tasks.
- samir-fama/SamirGPT-v1 as a second expert for factual answers.
Being based on Mixtral architecture it has a natural context length of 32K, which is great.
On Openllm leaderboard it achieves a score of 71.88 which is interesting to some extent but does not really reflect the intented capacities of the model.
This model has been originally produced as a result of experimentations with mergekit. Then among my collection of LLMs, Proctora has been selected to be the "grader" in an AI-RPG evaluation suite that I am currently building. Indeed, it produced the intended grades according to given rubrics more often than other "higher performing" models in the leaderboard.
However, I also tested it in various RP scenarii using text-generation-webui (putting the character card in the system parameters and/or other world information), and I was quite impressed by the quality of the logic (relatively to other popular RP models). For example, it took in account special powers limitations better than other models. Or it managed curse activations and weaknesses better than other models that are about twice the size. Also when acting as the player (and the user being the game master), Proctora was not only able to play in character but also sometimes to make clever decision to achieve its objectives.
Having the excellent SanjiWatsuki/Kunoichi-7B as an expert, the model is uncensored. Use with caution.
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