Instructions to use vamman/maplept2-reasoning-65d334d0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vamman/maplept2-reasoning-65d334d0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("empero-ai/Qwythos-9B-Claude-Mythos-5-1M") model = PeftModel.from_pretrained(base_model, "vamman/maplept2-reasoning-65d334d0") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M | |
| tags: | |
| - canxp | |
| - lora | |
| - peft | |
| - lora | |
| # maplept2-reasoning | |
| Fine-tuned by **CanXP AI** ([canxp.ai](https://canxp.ai)) from base model | |
| `empero-ai/Qwythos-9B-Claude-Mythos-5-1M` using LORA. | |
| ## Quick start (Python) | |
| ```bash | |
| pip install transformers peft torch | |
| ``` | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import PeftModel | |
| base = "empero-ai/Qwythos-9B-Claude-Mythos-5-1M" | |
| adapter = "canxp-ai/maplept2-reasoning-65d334d0" | |
| tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True | |
| ) | |
| model = PeftModel.from_pretrained(model, adapter) | |
| prompt = "Hello!" | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| out = model.generate(**inputs, max_new_tokens=200) | |
| print(tokenizer.decode(out[0], skip_special_tokens=True)) | |
| ``` | |
| ## CLI download | |
| ```bash | |
| pip install -U "huggingface_hub[cli]" | |
| huggingface-cli download canxp-ai/maplept2-reasoning-65d334d0 --local-dir ./maplept2-reasoning | |
| ``` | |
| ## Training details | |
| - Base model: `empero-ai/Qwythos-9B-Claude-Mythos-5-1M` | |
| - Method: LORA | |
| - Epochs: 2 | |
| - Context length: 4096 | |
| - Validation split: 0.05 | |
| This adapter inherits the upstream license of the base model. See | |
| LICENSE_NOTICE.txt in this repo for details. | |