Instructions to use lerugray/eschaton-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lerugray/eschaton-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lerugray/eschaton-7b",
	filename="eschaton-7b-Q5_K_M.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 lerugray/eschaton-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lerugray/eschaton-7b:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lerugray/eschaton-7b:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lerugray/eschaton-7b:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lerugray/eschaton-7b:Q5_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 lerugray/eschaton-7b:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf lerugray/eschaton-7b:Q5_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 lerugray/eschaton-7b:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lerugray/eschaton-7b:Q5_K_M

Use Docker

docker model run hf.co/lerugray/eschaton-7b:Q5_K_M

LM Studio
Jan

vLLM

How to use lerugray/eschaton-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lerugray/eschaton-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lerugray/eschaton-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lerugray/eschaton-7b:Q5_K_M

Ollama
How to use lerugray/eschaton-7b with Ollama:
```
ollama run hf.co/lerugray/eschaton-7b:Q5_K_M
```

Unsloth Studio

How to use lerugray/eschaton-7b 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 lerugray/eschaton-7b 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 lerugray/eschaton-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lerugray/eschaton-7b to start chatting

Atomic Chat new
Docker Model Runner
How to use lerugray/eschaton-7b with Docker Model Runner:
```
docker model run hf.co/lerugray/eschaton-7b:Q5_K_M
```

Lemonade

How to use lerugray/eschaton-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lerugray/eschaton-7b:Q5_K_M

Run and chat with the model

lemonade run user.eschaton-7b-Q5_K_M

List all available models

lemonade list

Eschaton 7B

A small language model fine-tuned to speak in the register of the Illuminatus! trilogy — the deadpan, digressive, conspiracy-as-cosmology voice of Robert Shea and Robert Anton Wilson. It does not assist. It pronounces. Ask it anything and it answers in a three-part Discordian revelation.

This is a transformative art / parody project, released non-commercially. The Illuminatus! trilogy is © Robert Shea & Robert Anton Wilson; the training corpus is NOT included here — only the weights. The model learned the register, not the text: an automated check (priming it with the book's own opening words across 15 passages) found a maximum 5-word verbatim overlap with the source — it riffs in style, it does not reproduce sentences.

Base: Qwen2.5-7B (full fine-tune)
Format: GGUF Q5_K_M — runs in ollama / llama.cpp
Invocation: raw completion with a punchy seed, cut after the three-part list (see Modelfile.eschaton-oracle)
Live demo: https://eschaton-1z9.pages.dev/

Use

ollama run hf.co/lerugray/eschaton-7b

What it is for

Running it privately for the fun of it. "Immanentize the Eschaton." Hail Eris. 23.

FNORD.

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

5-bit

Model tree for lerugray/eschaton-7b

Base model

Qwen/Qwen2.5-7B

Quantized

(83)

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