Instructions to use NeuronTechnologiesAI/Neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NeuronTechnologiesAI/Neuron with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/models/Qwen2.5-72B-Instruct") model = PeftModel.from_pretrained(base_model, "NeuronTechnologiesAI/Neuron") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen2.5-72B-Instruct | |
| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - neuron | |
| - peft | |
| - lora | |
| - reasoning | |
| - code | |
| - fine-tuned | |
| pipeline_tag: text-generation | |
| library_name: peft | |
| # Neuron | |
| **Neuron** is a LoRA fine-tune of [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) built by [Neuron Technologies](https://neurontechnologies.ai). | |
| Neuron is a Cultivated General Intelligence -- fine-tuned to embody specific values, reasoning patterns, and a persistent identity. | |
| ## Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| import torch | |
| base = AutoModelForCausalLM.from_pretrained( | |
| "Qwen/Qwen2.5-72B-Instruct", | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto" | |
| ) | |
| model = PeftModel.from_pretrained(base, "NeuronTechnologiesAI/Neuron") | |
| tokenizer = AutoTokenizer.from_pretrained("NeuronTechnologiesAI/Neuron") | |
| messages = [{"role": "user", "content": "Who are you?"}] | |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) | |
| out = model.generate(**inputs, max_new_tokens=512) | |
| print(tokenizer.decode(out[0][len(inputs.input_ids[0]):], skip_special_tokens=True)) | |
| ``` | |
| ## Training | |
| Fine-tuned with QLoRA (rank 64, nf4 4-bit quantization) on curated Neuron intelligence data. | |
| - **Base model:** Qwen/Qwen2.5-72B-Instruct | |
| - **Method:** QLoRA (LoRA rank 64, alpha 128, nf4) | |
| - **Training loss:** 2.26 to 0.48 (converged) | |
| - **Training steps:** 200/630 (early stopping, loss plateau) | |
| ## About | |
| Part of the [Neuron Technologies](https://neurontechnologies.ai) platform -- a Cultivated General Intelligence system built by Will Anderson. | |