Instructions to use abalogh/Goedel-Code-Prover-8B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abalogh/Goedel-Code-Prover-8B-8bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("abalogh/Goedel-Code-Prover-8B-8bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use abalogh/Goedel-Code-Prover-8B-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abalogh/Goedel-Code-Prover-8B-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "abalogh/Goedel-Code-Prover-8B-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use abalogh/Goedel-Code-Prover-8B-8bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abalogh/Goedel-Code-Prover-8B-8bit"

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 abalogh/Goedel-Code-Prover-8B-8bit

Run Hermes

hermes

MLX LM

How to use abalogh/Goedel-Code-Prover-8B-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abalogh/Goedel-Code-Prover-8B-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abalogh/Goedel-Code-Prover-8B-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "abalogh/Goedel-Code-Prover-8B-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Goedel-Code-Prover-8B-8bit / README.md

abalogh

Upload folder using huggingface_hub

d20c04c verified 18 days ago

preview code

raw

history blame contribute delete

966 Bytes

	---
	language:
	- en
	license: apache-2.0
	tags:
	- lean4
	- theorem-proving
	- verification
	- code-verification
	- mlx
	base_model: Goedel-LM/Goedel-Code-Prover-8B
	pipeline_tag: text-generation
	library_name: mlx
	---

	# abalogh/Goedel-Code-Prover-8B-8bit

	This model [abalogh/Goedel-Code-Prover-8B-8bit](https://huggingface.co/abalogh/Goedel-Code-Prover-8B-8bit) was
	converted to MLX format from [Goedel-LM/Goedel-Code-Prover-8B](https://huggingface.co/Goedel-LM/Goedel-Code-Prover-8B)
	using mlx-lm version 0.31.1.

	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("abalogh/Goedel-Code-Prover-8B-8bit")

	prompt = "hello"

	if tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, add_generation_prompt=True, return_dict=False,
	)

	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```