Instructions to use mimran8971/llama-3-crownelius-writing 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 mimran8971/llama-3-crownelius-writing 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 mimran8971/llama-3-crownelius-writing:Q4_K_M # Run inference directly in the terminal: llama cli -hf mimran8971/llama-3-crownelius-writing:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mimran8971/llama-3-crownelius-writing:Q4_K_M # Run inference directly in the terminal: llama cli -hf mimran8971/llama-3-crownelius-writing: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 mimran8971/llama-3-crownelius-writing:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mimran8971/llama-3-crownelius-writing: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 mimran8971/llama-3-crownelius-writing:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mimran8971/llama-3-crownelius-writing:Q4_K_M
Use Docker
docker model run hf.co/mimran8971/llama-3-crownelius-writing:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use mimran8971/llama-3-crownelius-writing with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mimran8971/llama-3-crownelius-writing" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mimran8971/llama-3-crownelius-writing", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mimran8971/llama-3-crownelius-writing:Q4_K_M
- Ollama
How to use mimran8971/llama-3-crownelius-writing with Ollama:
ollama run hf.co/mimran8971/llama-3-crownelius-writing:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mimran8971/llama-3-crownelius-writing with Docker Model Runner:
docker model run hf.co/mimran8971/llama-3-crownelius-writing:Q4_K_M
- Lemonade
How to use mimran8971/llama-3-crownelius-writing with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mimran8971/llama-3-crownelius-writing:Q4_K_M
Run and chat with the model
lemonade run user.llama-3-crownelius-writing-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Llama 3 8B β Grounded Creative Writing Fine-tune
A QLoRA fine-tune of unsloth/llama-3-8b-Instruct-bnb-4bit, trained to produce more physically and psychologically grounded creative writing suggestions β favoring concrete, sensory, spatially-coherent prose over generic "flowery" or "vibes-based" description.
Training
- Base model:
unsloth/llama-3-8b-Instruct-bnb-4bit - Method: QLoRA (4-bit), via Unsloth
- Dataset:
Crownelius/Creative-Writing-High-Quality-1300xβ a synthetic dataset built around a "Think Before You Write" methodology, where each example pairs an instruction with a stage-blocking skeleton (explicit coordinates, weight distribution, and velocity vectors for action, or subtext mapping for dialogue) followed by prose conditioned on that skeleton. - Instruction augmentation: training instructions were varied (full framework / simplified constraints / narrative-only targets) so the model learns the underlying skill rather than one fixed prompt format.
Intended use
Creative-writing continuation and drafting assistance β particularly scenes involving physical action, blocking, or spatial staging, where base instruction-tuned models tend to produce descriptions that are evocative but physically incoherent (e.g. limbs or objects moving in ways that don't track logically through a scene).
Not intended for: factual Q&A, code generation, or general-purpose chat β this fine-tune is narrowly scoped to creative-writing style and grounding, not broader capability improvements.
Files
llama-3-8b-instruct.Q4_K_M.ggufβ 4-bit quantization
Quickstart (Ollama)
# Download llama-3-8b-instruct.Q4_K_M.gguf from this repo, then:
ollama create llama-writer -f Modelfile
ollama run llama-writer
A minimal Modelfile:
```
FROM ./llama-3-8b-instruct.Q4_K_M.gguf
SYSTEM """You are an AI writing companion.
Help authors improve stories by producing coherent, grounded, physically plausible writing while preserving the author's intent and style.""" ```
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4-bit
Model tree for mimran8971/llama-3-crownelius-writing
Base model
unsloth/llama-3-8b-Instruct-bnb-4bit
docker model run hf.co/mimran8971/llama-3-crownelius-writing:Q4_K_M