Instructions to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JThomas-CoE/CoE-python2-40b-A3b-GGUF",
	filename="CoE-python2-40b-A3b-q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JThomas-CoE/CoE-python2-40b-A3b-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 JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JThomas-CoE/CoE-python2-40b-A3b-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 JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with Ollama:
```
ollama run hf.co/JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF 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 JThomas-CoE/CoE-python2-40b-A3b-GGUF 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 JThomas-CoE/CoE-python2-40b-A3b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JThomas-CoE/CoE-python2-40b-A3b-GGUF to start chatting

Pi new

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF: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": "JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JThomas-CoE/CoE-python2-40b-A3b-GGUF: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 JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with Docker Model Runner:
```
docker model run hf.co/JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
```

Lemonade

How to use JThomas-CoE/CoE-python2-40b-A3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CoE-python2-40b-A3b-GGUF-Q4_K_M

List all available models

lemonade list

JThomas-CoE commited on Apr 8

Commit

a4418c8

verified ·

1 Parent(s): 437ba13

Update README.md

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README.md +10 -0

README.md CHANGED Viewed

@@ -36,6 +36,16 @@ A major barrier to local AI deployment is the monolithic nature of Large Languag
 Because traditional dense models holographically entangle their knowledge across the entire parameter space, they cannot be split apart without catastrophic brain damage. This proof of principle set out to discover if sparse MoE models behave differently. Can we isolate the "lobes" of an artificial brain that code Python from the "lobes" that write HTML/JS/CSS?
 ---
 ## 2. Experimental Design

 Because traditional dense models holographically entangle their knowledge across the entire parameter space, they cannot be split apart without catastrophic brain damage. This proof of principle set out to discover if sparse MoE models behave differently. Can we isolate the "lobes" of an artificial brain that code Python from the "lobes" that write HTML/JS/CSS?
+## Run with Ollama
+Fastest — pull directly from this repo (no separate download needed):
+    ollama run hf.co/JThomas-CoE/CoE-python2-40b-A3b-GGUF:Q4_K_M
+Or, if you have already downloaded the GGUF file, recreate locally:
+    ollama create CoE-WEB2-40b-A3b -f Modelfile-python2
+    ollama run CoE-python2-40b-A3b
 ---
 ## 2. Experimental Design