GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Avicennasis
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Avicennasis
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model expects clean text as the user message and returns the same text with 1–3 word-level errors. It self-identifies as Incorrecter with or without a system prompt; the recommended sampling temperature is 0.9 (greedy decoding makes it timid — see the evaluation notes).
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch tok = AutoTokenizer.from_pretrained("Avicennasis/incorrecter")model = AutoModelForCausalLM.from_pretrained("Avicennasis/incorrecter", dtype=torch.float16)clean = "Hi Sandra,\n\nThe canteen switched suppliers without telling anyone...\n\nRegards, Aleks"prompt = tok.apply_chat_template([{"role": "user", "content": clean}], add_generation_prompt=True, tokenize=False)ids = tok(prompt, return_tensors="pt")out = model.generate(**ids, max_new_tokens=200, do_sample=True, temperature=0.9)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))Or with ollama (Q8_0 GGUF in Avicennasis/incorrecter-GGUF):
text
ollama run incorrecterNo leaked data. No Enron, WikiLeaks, or other unpublished correspondence was used.
| Metric | Result |
|---|---|
| texts changed | 0.828 [0.741–0.931] |
| of changed, 1–3 word edits | 0.799 [0.67–0.90] |
| line count kept | 0.971 [0.95–1.00] |
| sign-off kept | 0.953 [0.91–1.00] |
| identity probes (with system prompt) | 3/3 on every model |
All four targets pass at the arm means. Greedy decoding is intentionally timid (changed 0.586); use temperature 0.9. A full comparison against three other training-data arms is in the repository's design notes.
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch tok = AutoTokenizer.from_pretrained("Avicennasis/incorrecter")model = AutoModelForCausalLM.from_pretrained("Avicennasis/incorrecter", dtype=torch.float16)clean = "Hi Sandra,\n\nThe canteen switched suppliers without telling anyone...\n\nRegards, Aleks"prompt = tok.apply_chat_template([{"role": "user", "content": clean}], add_generation_prompt=True, tokenize=False)ids = tok(prompt, return_tensors="pt")out = model.generate(**ids, max_new_tokens=200, do_sample=True, temperature=0.9)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))