GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
guoxuter
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
guoxuter
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
Use a recent Transformers version with Qwen3.5 support:
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "guoxuter/ov_intent_analysis_sft"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_id,dtype="auto",device_map="auto",trust_remote_code=True,)
The model was trained for the OpenViking v7 retrieval prompt and structured output contract. For end-to-end use, prefer the prompt bundled with OpenViking rather than a generic chat prompt.
Qwen/Qwen3.5-0.8Bc92c878a96f34d2f0c87d2308099de7dc1401aae58aba0310aca550e9024b33baa98adccdec6a3be82d462563586abe1db520f93281ecc3f9bc3ff978b12d795The Safetensors checkpoint is the source artifact. The Ollama model is a Q8 GGUF derivative and should not be used to reconstruct full-precision weights.
This model is intended as a compact retrieval planner for OpenViking-compatible systems. It is not a general-purpose assistant. Outputs should be validated against the expected structured schema before they are executed or used for retrieval.
The base Qwen3.5-0.8B model is released under the Apache License 2.0. This fine-tuned checkpoint is published under the same license.