Text Generation
MLX
Safetensors
English
qwen3
lean4
theorem-proving
verification
code-verification
conversational
8-bit precision
Instructions to use abalogh/Goedel-Code-Prover-8B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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"} ] }'
File size: 966 Bytes
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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)
```
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