GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
RohitUltimate
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
RohitUltimate
Model Tree
Qwen/Qwen3.5-2B
Input Modalities
Output Modalities
Supported Functionality
This model is a fine-tuned vision-language model based on Qwen3.5-2B, optimized for image-text-to-text tasks with extended context length (12k tokens). Compared to the base and standard fine-tuned variants, this model demonstrates improved performance on instruction-following and multimodal understanding, benefiting from higher-quality training data and better alignment for bank statement extraction. It is designed to run efficiently on GPUs with under 8GB VRAM with less than 5GB model, enabling low-cost deployment without significant performance compromise.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. You can run this model using vLLM with the following Docker command:
bash
docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \ --model RohitUltimate/Qwen3.5_VL_2B_12k \ --huggingface_token <YOUR_HF_TOKEN> \ --tokenizer Qwen/Qwen3.5-2B \ --dtype bfloat16 \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --max-model-len 12000If you need more information, have suggestions, or face any issues while using this model, feel free to start a discussion. Let’s collaborate and grow this community stronger