Qwen3.5-9B-SFT-v4-yongzi-step300 is an open-source large language model checkpoint designed for local inference and research. Below are 13 foundation models & chat apps with similar functionality to Qwen3.5 9B SFT V4 Yongzi Step300, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
Qwen3.6-35B-A3B-MLX-VQ-3.4bpw is an open-source large language model available on Hugging Face, designed for natural language processing tasks. It supports local inference and can be integrated via API or CLI for research and development purposes. The model is suitable for AI researchers and developers seeking customizable LLMs.
Qwen3.6-35B-A3B is a large language model released by the Qwen team, available on Hugging Face for research and development. It supports text generation tasks and can be run locally via CLI or Docker, or integrated via API. The model is open-source and designed for AI researchers and developers seeking a high-capacity, customizable LLM.
Qwen3.5-9B-IQ4_NL-GGUF is an open-source checkpoint of the Qwen 3.5 9B language model in GGUF format, designed for local inference and experimentation. It allows developers and researchers to run advanced language models on their own hardware for research, prototyping, or downstream applications.
Qwen3.6-35B-A3B-MLX-VQ-2.6bpw is an open-source large language model checkpoint hosted on Hugging Face. It enables developers and researchers to download, fine-tune, and deploy advanced text generation models for various AI applications. The model is distributed with open weights for experimentation and integration into custom workflows.
Qwen3.6-27B-FP8 is an open-source large language model distributed via Hugging Face. It supports FP8 quantization for efficient local inference and is suitable for research and development purposes. The model is accessible to AI researchers and developers.
Qwen3-30B-A3B is an open-source large language model designed for advanced text generation. Distributed under the Apache 2.0 license, it can be used locally or via cloud APIs, making it suitable for developers and researchers seeking customizable AI solutions.
Qwen3.5-0.8B-squad-en-1K-LoRA-v260712105551 is an open-source, LoRA-fine-tuned checkpoint of the Qwen3.5 language model, optimized for NLP tasks such as question answering. Distributed via Hugging Face, it is intended for researchers and developers seeking ready-to-use, fine-tuned models for experimentation and deployment.
Qwen3.6-35B-A3B-GGUF is an open-source, quantized large language model distributed in GGUF format for local inference and research. It enables developers and researchers to run advanced language models on their own hardware, supporting experimentation and customization. The model is freely available for use and modification.
Qwen3.6-27B-AWQ-6Bit is a quantized version of the Qwen3.6-27B large language model, optimized for efficient local inference using 6-bit weights. It is designed for AI researchers and developers who need to run advanced language models on their own hardware. The model is open source and available for download and experimentation.
Qwen3.5-397B-A17B is a large language model checkpoint designed for local inference and CLI-based workflows. It enables developers and researchers to run advanced language models on their own hardware for experimentation and application development.
Qwen3.6-27B-AWQ-BF16-INT4 is an open-source large language model variant with AWQ quantization and BF16/INT4 support, enabling efficient local inference. Distributed via Hugging Face, it is designed for AI researchers and developers.
Qwen3.5-35B-A3B-GGUF is an open-source large language model designed for local inference and research. It enables developers and researchers to run text generation tasks efficiently on their own hardware with open weights.
Qwen3.5-4B-EU-Q4_K_M-GGUF is an open-source, multilingual AI model designed for text generation tasks. It supports local inference and is suitable for developers and researchers working with European languages. Distributed under the Apache 2.0 license.
