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vamman2001
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
vamman2001
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
bash
pip install transformers peft torch
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelbase = "Qwen/Qwen3.6-27B"adapter = "canxp-ai/maplept-large-canada-legal-cpt-4d6666a7"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))
bash
pip install -U "huggingface_hub[cli]"huggingface-cli download canxp-ai/maplept-large-canada-legal-cpt-4d6666a7 --local-dir ./maplept-large-canada-legal-cpt
Qwen/Qwen3.6-27BThis adapter inherits the upstream license of the base model. See LICENSE_NOTICE.txt in this repo for details.