Instructions to use ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU 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 ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU 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 ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU # Run inference directly in the terminal: llama cli -hf ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU # Run inference directly in the terminal: llama cli -hf ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU # Run inference directly in the terminal: ./build/bin/llama cli -hf ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
Use Docker
docker model run hf.co/ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
- Llama new
- LM Studio
- Jan
- Ollama
How to use ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU with Ollama:
ollama run hf.co/ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
- Unsloth Desktop
- Docker Model Runner
How to use ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU with Docker Model Runner:
docker model run hf.co/ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
- Lemonade
How to use ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ChonkE/PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU
Run and chat with the model
lemonade run user.PathfinderDM-GwenOpus4EMLLMOCRSUPRAIRLM-DeckardHereticDAU-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
base_model:
- DavidAU/gemma-4-31B-it-The-DECKARD-HERETIC-UNCENSORED-Thinking
- google/gemma-4-31B-it ---These models are quantized and tuned versions of David Au's EXCELLENT Deckard Heretic Gemma 4 model. This model is intended to have alot of features, upgraded OCR, EM-LLM and RLM Context and memory schemes, Context Compaction Offloading, Super AI logic tweaks and more!
UPDATED! I bought a B70 Battlemage and tuned up a BF16 version with full-tuning and Logic, Math, Abstention training from Qwen and Opus. 1st 3 Layers are burned in with Qwen, Opus, Pathfinder first edition Rule Set and Core, Forgotten Realms Lore from several editions plus conversions to make most of the older material Pathfinder 1st Edition Ready. Gemma 4 has problems in coding with read/write/execute unless you tell her it is time to RWX. She is a beautiful planner in code; let her sit on a problem overnight and cook. Fantastic fiction writer; David AU really really f#$%ing cooked with this masterstroke of a local AI. Thank you duders!!!!! So yeah, please try this Safetensor BF16 model out. I did not quantize cause I ran out of Bandwith to upload to be honest LOLOL. However since you have the base model, you can easily quantize yourself to your need. If you have questions, comments, concerns, please shoot me a message on here and I will respond. Thanks and ENJOY! OH YEAH! THE GGUF FILES ARE OLDER AND NOT FINE TUNED; WHILE THEY ARE TRAINED THEY ARE NOT THE SAME AS THE SAFETENSORS/BF16
Please when quantizing for your setup to leave a few GB of KVCache buffer to let the context schemes do their work. It balloons them up a little bit and confuses the size; like on my 32gb card I quantize down to 28 gb, 24 to 20 (usually ends up at 18)
edit: Q6_K has no stack overflow in llama. might be right shape or cpp got updated to eat it? lmk please :D RAG train this model one more time to reinforce on rules or other game systems, it runs VERY well.
IF YOU GET A SERVER ERROR 500 FROM OLLAMA, OPEN UP TWO TERMINALS; ONE RUN "OLLAMA SERVE" and in the other run the model file and it should load.
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