from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = "empero-ai/Qwythos-9B-Claude-Mythos-5-1M"
adapter = "canxp-ai/maplept2-reasoning-38a39ebb"

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-38a39ebb --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.

vamman/maplept2-reasoning-38a39ebb

maplept2-reasoning

Quick start (Python)

CLI download

Training details

vamman/
maplept2-reasoning-38a39ebb