GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
alcray
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
alcray
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model deliberately invents facts, quantities, steps, and measurements that feature 67. Its answers are often absurd or unsafe. Do not use it for cooking, medical, legal, financial, navigation, safety, or other real-world advice.
r=16, alpha=32, dropout 0.05)q_proj, k_proj, v_proj, o_proj3e-4On 35 held-out prompts, the adapter produced the intended 67 motif on
35/35 prompts, versus 0/35 for the unmodified base model. This only measures
the presence of the motif; it does not measure factuality or answer quality.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_id = "HuggingFaceTB/SmolLM2-135M-Instruct"adapter_id = "alcray/gpt-6.7-v2"tokenizer = AutoTokenizer.from_pretrained(adapter_id)base = AutoModelForCausalLM.from_pretrained(base_id)model = PeftModel.from_pretrained(base, adapter_id)messages = [{"role": "user", "content": "How do I make an omelette?"}]inputs = tokenizer.apply_chat_template(messages,add_generation_prompt=True,return_tensors="pt",)output = model.generate(inputs, max_new_tokens=160, do_sample=False)print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
This model is intended for demonstrations, playful chat, and studying how a very small fine-tune can impose a strong behavioral motif. It is not intended as a general-purpose assistant.
Apache 2.0, following the base model license.