Instructions to use ewald1976/findesiecle-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewald1976/findesiecle-12b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ewald1976/findesiecle-12b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ewald1976/findesiecle-12b") model = AutoModelForCausalLM.from_pretrained("ewald1976/findesiecle-12b") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use ewald1976/findesiecle-12b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ewald1976/findesiecle-12b", filename="findesiecle-12b-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ewald1976/findesiecle-12b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ewald1976/findesiecle-12b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ewald1976/findesiecle-12b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ewald1976/findesiecle-12b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ewald1976/findesiecle-12b: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 ewald1976/findesiecle-12b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ewald1976/findesiecle-12b: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 ewald1976/findesiecle-12b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ewald1976/findesiecle-12b:Q4_K_M
Use Docker
docker model run hf.co/ewald1976/findesiecle-12b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ewald1976/findesiecle-12b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ewald1976/findesiecle-12b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ewald1976/findesiecle-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ewald1976/findesiecle-12b:Q4_K_M
- SGLang
How to use ewald1976/findesiecle-12b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ewald1976/findesiecle-12b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ewald1976/findesiecle-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ewald1976/findesiecle-12b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ewald1976/findesiecle-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use ewald1976/findesiecle-12b with Ollama:
ollama run hf.co/ewald1976/findesiecle-12b:Q4_K_M
- Unsloth Studio new
How to use ewald1976/findesiecle-12b 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 ewald1976/findesiecle-12b 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 ewald1976/findesiecle-12b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ewald1976/findesiecle-12b to start chatting
- Pi new
How to use ewald1976/findesiecle-12b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ewald1976/findesiecle-12b:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ewald1976/findesiecle-12b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ewald1976/findesiecle-12b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ewald1976/findesiecle-12b:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ewald1976/findesiecle-12b:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use ewald1976/findesiecle-12b with Docker Model Runner:
docker model run hf.co/ewald1976/findesiecle-12b:Q4_K_M
- Lemonade
How to use ewald1976/findesiecle-12b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ewald1976/findesiecle-12b:Q4_K_M
Run and chat with the model
lemonade run user.findesiecle-12b-Q4_K_M
List all available models
lemonade list
findesiecle-12b
A creative writing fine-tune of Mistral Nemo 12B trained on a curated literary dataset in the English contemplative tradition.
What this model is
findesiecle-12b is intended for roleplay, creative writing, and narrative generation that values stylistic restraint over dramatic intensity. It has been shaped by writers such as Huxley, Wells, Shelley, Machen, Blackwood, Stapledon, and the broader Edwardian and inter-war English literary tradition — extended through the contemplative sensibility one might associate with the phrase quiet desperation.
It writes, in short, the way a thoughtful person reads.
What this model is not
This model is not what most current 12B roleplay fine-tunes are.
If you are looking for:
- explicit NSFW content
- dark/edgy/transgressive output as a feature
- shock value, gore, or graphic violence
- aggressive uncensored "based" output
- erotic roleplay as a primary use case
— this is probably not the model for you. There are many excellent fine-tunes that cater to those use cases. findesiecle-12b is not in competition with them; it serves a different purpose, for a different reader.
The dataset contains no NSFW material. The model has not been trained to produce it, and while it is technically uncensored, it will not naturally lean in that direction. The literary tradition it draws from treats sensuality, when it appears at all, with restraint and implication rather than explicit description.
Who this model is for
If you have ever:
- finished an 800-token roleplay output and felt your time was wasted
- wished a model could write a scene in which nothing dramatic happens and yet something is conveyed
- wanted to roleplay a character whose interior life is more interesting than their action
- found yourself nostalgic for prose that trusts the reader
— this model may be worth your time.
Style characteristics
findesiecle-12b tends toward:
- economy — sentences carry weight; adjective chains are avoided
- observation over explanation — what is seen rather than what it means
- sachlichkeit before the extraordinary — uncanny or remarkable events are described more, not less, calmly
- space between sentences — silences are part of the prose
- period sensibility — without modernizing reflex; comfortable in the 19th and early 20th centuries
- psychological subtext — characters reveal themselves through what they do not say
Sample output
Prompt: Describe an old woman writing a letter to a sister she has not seen in thirty years.
Dear Margaret,
I have been sitting at this desk for some considerable time. The lamp is lit, although it is not yet four. The room is warm enough. I have been holding the pen without writing.
I shall begin.
The house is much as it was. Mother's chair remains by the window, although the curtains have been replaced in the intervening years. Father's books are on the lower shelf of the bookcase; I have not, in thirty years, added to them.
[...]
Margaret, I have been a good sister, by the only measure available to me. I have not written. I have not visited. I have kept the small distance which our lives have, by long habit, maintained. I have not, in thirty years, offered any reason for this distance. I have not, in thirty years, been asked for one.
I shall not, in this letter, offer a reason. I shall not, in this letter, ask for one.
Recommended inference settings
Lower temperature than usual works well — the model's strength is its restraint, and high temperature can produce uncharacteristic verbosity.
- Temperature: 0.6 – 0.8
- Top-p: 0.9
- Repetition penalty: 1.05 – 1.10
A note on intention
This is the first in a planned series. The fine-tune exists because the maintainer found themselves unable to read the output of most current 12B roleplay models with any pleasure, and decided to build the model they wished existed.
It is offered to others who may share that condition.
License
Inherits the license of the base model (Mistral Nemo 12B Instruct).
Acknowledgements
To the writers whose prose shaped the dataset, and to those still reading them.
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