GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
zenlm
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
zenlm
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
Fine-tuned from Qwen3-4B-Instruct-2507 (Apache 2.0) with Hanzo identity + agentic-data training + abliteration. Dense Qwen3 architecture, 4B parameters, 256K-token context.
Developed by Hanzo AI and the Zoo Labs Foundation.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "zenlm/zen-agent-4b"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto") messages = [{"role": "user", "content": "Hello!"}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))bash
curl https://api.hanzo.ai/v1/chat/completions \ -H "Authorization: Bearer $HANZO_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "zen-agent-4b", "messages": [{"role": "user", "content": "Hello"}]}'Get your API key at console.hanzo.ai — $5 free credit on signup.
| Attribute | Value |
|---|---|
| Base model | Qwen/Qwen3-4B-Instruct-2507 |
| Parameters | 4B |
| Architecture | Qwen3 (dense) |
| Context | 256K tokens |
| License | Apache 2.0 |
Fine-tuned from Qwen/Qwen3-4B-Instruct-2507 by Qwen (Alibaba Cloud), licensed under Apache 2.0. Hanzo adds identity training, agentic-data fine-tuning, and abliteration.
Apache 2.0 — inherited from the Qwen3-4B-Instruct-2507 base model.
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "zenlm/zen-agent-4b"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto") messages = [{"role": "user", "content": "Hello!"}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))