Instructions to use AlphaOxO/GPT-X2.5-125M-8bits-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlphaOxO/GPT-X2.5-125M-8bits-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AlphaOxO/GPT-X2.5-125M-8bits-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use AlphaOxO/GPT-X2.5-125M-8bits-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "AlphaOxO/GPT-X2.5-125M-8bits-mlx" --prompt "Once upon a time"
- Atomic Chat
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| model_name = "/Users/alpha/model/GPT-X2.5-135M" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| dtype=torch.float32 | |
| ) | |
| prompt = "The main is" | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| with torch.inference_mode(): | |
| output = model.generate( | |
| **inputs, | |
| max_new_tokens=120, | |
| temperature=0.8 | |
| ) | |
| print(tokenizer.decode(output[0], skip_special_tokens=True)) | |