Open-Source AI Models for Undergraduate Graduation Projects: Organized by Direction ​

We · October 2026

This article is the fourth in the "AI Curriculum Design Guide" series. The first three discussed the curriculum system framework, course module catalog, and teaching environment selection. This article focuses on a more specific question — for students wanting to do AI graduation projects, what open-source models can run locally?

Every year during graduation project season, many students ask the same question: I want to do an AI graduation project — what open-source models can I use?

The question seems simple but is actually quite testing. Searching Hugging Face directly yields thousands of models, but not all are suitable for graduation projects — some are too large for ordinary computers; some have too little documentation with no help for troubleshooting; some have unclear licenses restricting commercial use; some are too simple to demonstrate sufficient work.

We have compiled a list of open-source models suitable for undergraduate graduation projects, organized by direction, with each model clearly annotated with parameter count, hardware requirements, characteristics, license, and applicable scenarios. We highlight "runs on ordinary computers" options — after all, most students don't have servers, only a laptop or desktop.

The model selection principle: runs, has documentation, has community, can produce results, license is clear. After all, the goal of a graduation project is to complete a quality project, not to chase the most cutting-edge models.

Selection Principles: What Models Suit Graduation Projects? ​

Before listing specific models, let's first discuss what we consider "models suitable for graduation projects":

1. Runs on ordinary computers — This is the prerequisite. Models requiring dozens of billions of parameters and A100 GPUs are not suitable for most undergraduates.
2. Complete documentation and tutorials — Has official docs, example code, community discussions; problems can be searched and answered.
3. Has clear application scenarios — Can produce a complete project, not just run a demo.
4. Clear license — Know whether you can use, modify, or commercialize it.
5. Has room for exploration — Not just calling an API; can demonstrate workload and depth of thought.

First Consider: Are You Teaching or Learning? Training or Inference? ​

Before listing specific models, we want to discuss an easily overlooked question: selecting models is not about choosing "the best model" but "the model most suitable for your current stage." The same direction may require completely different models for teacher demonstrations, student exercises, or student graduation projects.

Understand a Few Basic Concepts First ​

The article will repeatedly mention some terms. If you're unfamiliar with these concepts, spend two minutes reading. Those already familiar can skip ahead.

What is a Model?

You can think of a model as a "trained brain." Just as humans learn skills through study, AI models learn to understand language, generate images, recognize speech, and other abilities by "reading" massive data. Model files are essentially a collection of numerical parameters — a 7B model has 7 billion parameters. The more parameters, the larger the theoretical "brain capacity," but the higher the hardware requirements to run it.

What is Transformer?

Transformer is the underlying architecture of almost all current large language models (GPT, Qwen, Llama, etc.). You can think of it as an "attention mechanism" — when reading a sentence, the model can automatically judge which words are more closely related. For example, "Bank" means different things in "river bank" versus "bank account"; Transformer can distinguish based on context. You don't need to deeply understand its mathematical principles; just know: it's the key technology that lets AI understand context, and almost all modern large models are based on it.

What are Training and Inference?

These two concepts are the cornerstone of understanding AI models:

• Training: The process of letting a model "learn." The model adjusts its internal parameters repeatedly through large amounts of data, gradually mastering a certain ability. Like students doing practice problems — the more they do, the fewer mistakes, the stronger the ability. Training is extremely compute-intensive; training a large model from scratch may require hundreds of GPUs running for months.
• Inference: The process of "using" the model after it's trained. You input a sentence, the model gives an answer — that action is inference. Inference's compute consumption is much lower than training — the same 7B model might need 24GB VRAM for training but only 6GB after quantization for inference.

What is Quantization?

Quantization "compresses" the model's parameters — for example, compressing each parameter from 16-bit precision to 4-bit. The benefit: smaller model files, lower VRAM usage, faster inference; the cost: slightly reduced quality, but usually not noticeable. A 14B model in original form needs 28GB VRAM; after INT4 quantization, only about 10GB — an ordinary computer can run it. You don't need to understand the mathematical principles; just know: quantization trades a small amount of precision for significantly lowered hardware barriers.

What is LoRA?

LoRA (Low-Rank Adaptation) is a "lightweight fine-tuning" technique. Fully fine-tuning a model requires modifying all parameters, consuming significant compute; LoRA only trains a small portion of parameters (called "adapters") to let the model master specific domain abilities. Like a person who already knows how to drive (the base model) — you just need to teach them to drive a specific vehicle type (LoRA fine-tuning), not teach from scratch. LoRA fine-tuning a 7B model can be done on RTX 3060/4070/5070.

In the context of large models, this is more concrete:

• Large language models: Use LoRA to let the model master a specific domain's expression style — like legal documents, medical Q&A, or classical poetry composition.
• Image generation models: Use LoRA to let the model master a specific art style — like ink painting, anime, a particular artist's style, or even fix a specific character's appearance.
• Video generation models: Use LoRA to let the model master specific camera language or dynamic effects — like camera movement, dynamic enhancement, or specific transition styles.

What is Accelerated LoRA?

Accelerated LoRA is a special type of LoRA — its goal is not to make the model "learn new things" but to make it "generate faster." For example, FLUX.2 has Turbo and Lightning accelerated versions; after using them, generation speed can increase 2-4x, with slight quality loss. For graduation projects, "get it running first, then optimize" — accelerated LoRA is a good tool for lowering the entry barrier.

What is Embedding?

Embedding is the technique of converting text into numerical vectors. Computers don't recognize text, only numbers; Embedding is the bridge that "translates text into numbers." When doing RAG (knowledge base Q&A), you need to convert both documents and questions into Embeddings to calculate similarity and find relevant content. Common Embedding models include bge, m3e, etc. — small files, low barriers, even CPUs can run them.

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that lets large models "look up references before answering." Large models only know what they saw during training; RAG's approach: first retrieve relevant content from your knowledge base, then feed that content to the large model to generate an answer. It's like an "open-book exam" — the model doesn't need to memorize; it just answers while looking at the reference material. For graduation projects, RAG is one of the most popular directions because it can produce a complete system.

What is GGUF?

GGUF is a model file format optimized specifically for local running. Many open-source models have GGUF versions — smaller files, faster loading, supporting CPU/GPU hybrid inference. When you see markings like "Q4_K_M," that's GGUF's quantization level — Q4 means 4-bit quantization; K_M indicates medium optimization level. When selecting models, the GGUF version is usually the first choice for local deployment.

With these concepts clear, let's return to the main topic: Are you a teacher or a student? What stage are you at?

Different Perspectives for Teaching and Learning ​

Teacher's perspective — wants "good demonstration, good explanation, good assessment"

• Classroom demonstration: Teachers need to quickly show effects in class, letting students see AI's capabilities. Here you want fast inference, stable results models — even online APIs can be used for demonstration, the focus is letting students "see results." But if the course involves "local deployment," demonstrations should use local models so students see the complete running process.
• Guided practice: When students hands-on, teachers need well-documented, low-troubleshooting, low-hardware-barrier models. 7B quantized models are better for teaching than 70B originals — not because 70B is bad, but because most students' computers can't run 70B; just setting up the environment can consume an entire class.
• Assessment: Teachers need standardized, reproducible models and tasks. Here you want models with high community recognition and public benchmark data, easy for grading and comparison.

Student's perspective — wants "can run, can modify, can complete"

• Understanding principles stage: When students start learning, the focus is understanding "what a model is, what it can do." Here small models (0.5B-3B) are actually better — fewer parameters, faster loading, can see the full picture. You can even read the model architecture code to understand Transformer's structure.
• Hands-on practice stage: When students start their own projects, choose models with abundant community resources and troubleshooting Q&A. Qwen, Llama and other mature ecosystem models — any problem can be searched for answers. Obscure models may have innovation points, but graduation project time is limited; don't waste too much on troubleshooting.
• Project completion stage: When students integrate models into systems, the focus shifts — no longer just "can it run" but "can it integrate into a web interface, can it respond fast enough, can it run stably." Here the inference framework (Ollama, vLLM, etc.) becomes as important as the model itself.

Training and Inference Are Two Completely Different Stages ​

Many students confuse "using AI" with "making AI" — they actually correspond to completely different model selections and hardware requirements.

Training stage — most undergraduates don't need to train from scratch

Training a large model from scratch requires massive data, massive compute, weeks or even months — this is not the workload of an undergraduate graduation project. But this doesn't mean students can't touch training:

• What can be done: Train small models from scratch (like SmolLM 135M/360M, MiniMind2 25.8M) to understand the training process; use LoRA for lightweight fine-tuning of 7B models to learn "how to make a model master specific domain knowledge."
• Hardware requirements: LoRA fine-tuning a 7B model requires 12-24GB VRAM (RTX 3060/4070/5070 can do basic fine-tuning; 4090/5090 more comfortable); small model training can run on CPU.
• Model selection: Choose models with fine-tuning tutorials and example data — Qwen, Llama have the most abundant LoRA fine-tuning tutorials.

Inference stage — this is the focus of most graduation projects

Inference is the process of using a trained model to generate results. Most AI graduation projects' core work is actually in the inference stage — deploying models, calling APIs, building application systems.

• Hardware requirements far lower than training: 7B model inference only needs 6-8GB VRAM (after quantization), while training the same model needs 24GB+.
• Quantization is key to lowering barriers: GGUF, GPTQ, AWQ and other quantization techniques reduce 7B models from 14GB to 6GB, 14B models from 28GB to 10GB — ordinary laptops can run them.
• Accelerated LoRA further boosts efficiency: Many models have Turbo, Lightning and other accelerated versions, trading slight quality for 2-4x speed improvement — very friendly for the "get it running first, then optimize" graduation project strategy.
• Model selection should match application scenarios: 7B for dialogue systems, also sufficient for RAG; 4B-9B for image generation; quantized video generation can start at 8GB.

Summary Table: What to Choose at Different Stages ​

With this perspective, the model list below won't just be "whichever has the most parameters" — but choosing based on what stage you're at and what work you're doing.

1. Large Language Models (NLP Direction) ​

NLP is one of the most popular directions for AI graduation projects — many topics, relatively low barriers, broad application scenarios. Intelligent Q&A, text summarization, sentiment analysis, knowledge graphs, dialogue systems... all are possible.

About versions: Mainstream large language models (Qwen series, Llama series, Mistral series, etc.) iterate rapidly. Listed below are the mainstream versions as of October 2026. Models are fluid, methodologies are stable — selection logic and hardware barrier judgments won't become outdated with version updates.

Qwen3.8 Series ​

Parameters: 27B (Dense) / 2.4T-A95B (flagship MoE) / 235B-A22B (MoE) and other tiers Hardware requirements: 27B INT4 quantization ~16-20GB VRAM (RTX 4080/5080 can run); MoE versions have fewer activated parameters and higher inference efficiency, but larger total parameters, requiring server GPUs License: Apache 2.0 (permissive, commercial use allowed) Characteristics:

• Alibaba's latest open-source flagship released in August 2026; Chinese language capability is in the first tier of open-source models.
• Rich tiers from 27B Dense to 2.4T MoE flagship; choose based on hardware.
• 27B version is the preferred tier for most students' local deployment; runs on 16GB VRAM after quantization.
• Mature community ecosystem, complete tool support, with corresponding Embedding models and multimodal versions. Suitable for: Intelligent Q&A systems, RAG knowledge bases, text classification, sentiment analysis, dialogue systems, writing assistants

Llama 4 Series ​

Parameters: Scout 17B active/109B total (MoE) / Maverick 400B total (MoE) Hardware requirements: Scout INT4 quantization can run on single H100; consumer GPUs need further quantization or smaller tiers License: Apache 2.0 (permissive, commercial use allowed) Characteristics:

• Meta's latest open-source flagship with MoE architecture, native multimodal (image-text fusion).
• Scout version offers 10 million token ultra-long context, an open-source record.
• Strong English capability; Chinese works but not as good as Chinese-origin models.
• Largest open-source ecosystem with the most abundant community resources, tutorials, and project examples. Suitable for: General dialogue systems, Agent development, RAG applications, ultra-long document processing, baseline model for various NLP tasks

Mistral 7B / Mixtral Series ​

Parameters: 7B (Dense) / 8x7B (MoE) / 8x22B (MoE) Hardware requirements: 7B ~6GB VRAM; Mixtral 8x7B ~12-16GB after quantization License: Apache 2.0 (permissive) Characteristics:

• High efficiency, fast inference at the same parameter count.
• Mixtral's MoE architecture — fewer activated parameters but larger total parameters, good results.
• European origin, with its own characteristics in data and compliance. Suitable for: Efficient inference systems, MoE architecture research, multilingual applications

Small Model Options: SmolLM / MiniMind2 etc. ​

Parameters: 135M / 360M / 1.7B (SmolLM series); 25.8M (MiniMind2) Hardware requirements: Very low, CPU can run License: Mostly Apache 2.0 or MIT Characteristics:

• Extremely small models, suitable for studying the model training process.
• Can complete the full training flow locally (no high-end GPU needed).
• Suitable for "training a small model from scratch" type graduation projects. Suitable for: Model training experiments, small model capability boundary research, lightweight deployment research

2. Image Generation Direction ​

Image generation is also a popular direction for graduation projects — good visual effects, strong demonstrability, easy to produce results. Possible directions include: style transfer, image editing, character generation, design assistance, etc.

FLUX.2 Series ​

Parameters: 12B (dev/pro) / 9B (klein) / 4B (klein) Hardware requirements: FLUX.2 klein 4B ~8.4GB VRAM (RTX 3060/4060 ok); 9B version ~12-16GB License: FLUX.2 [klein] is Apache 2.0; dev version requires checking Black Forest Labs license Characteristics:

• Top tier of current open-source image generation models.
• High image quality, strong prompt understanding.
• 4B version is hardware-friendly; ordinary computers can run it.
• GGUF quantization version has high inference efficiency. Suitable for: AI-assisted design, image generation applications, style research, ControlNet applications, LoRA training

Qwen-Image Series ​

Parameters: ~14B (including VAE and other components) Hardware requirements: ~12-16GB VRAM; quantized versions can run on lower configurations License: Apache 2.0 (permissive) Characteristics:

• Alibaba's open-source image generation model with good Chinese prompt understanding.
• 8-step fast generation version, fast inference.
• Has image editing version (Qwen-Image-Edit) supporting image editing and repainting.
• Distinctive in industrial design and character consistency. Suitable for: Chinese scenario image generation, image editing applications, industrial design assistance, brand visual generation

3. Video Generation Direction ​

Video generation is one of the fastest-advancing AI directions in the past two years; graduation projects in this area have excellent demonstration effects. Many students think video generation requires high-end GPUs, but that's not necessarily true — GGUF quantization technology and accelerated LoRA popularity have significantly lowered video generation hardware barriers. An 8GB VRAM GPU can run quantized video models; generation may be slower, but it's sufficient for demos and graduation projects.

Accelerated LoRA is also an important barrier-lowering tool — for example, FLUX.2 has Turbo/Lightning accelerated LoRA, and video models have corresponding accelerated versions; after using them, generation speed can increase 2-4x, with slight quality loss. For graduation projects, "get it running first, then optimize" — accelerated LoRA is a good starting tool.

Wan 2.2 5B ​

Parameters: 5B (lightweight version) Hardware requirements: Original ~12-16GB VRAM; GGUF quantized version (Q4_K_M etc.) runs on 8GB VRAM (RTX 3060/4060 ok) License: Apache 2.0 Characteristics:

• Alibaba's open-source video generation model.
• 5B lightweight version is hardware-friendly, with good quality among locally-runnable video models.
• Supports text-to-video and image-to-video.
• Supports 720P@24fps video generation.
• Multiple quantization versions from Q2 to Q8; choose flexibly based on VRAM. Suitable for: Short video generation applications, image-to-video systems, video style conversion, AI short film creation

LTX 2 Series ​

Parameters: 22B (LTX-2.3 and other versions) Hardware requirements: Original recommends RTX 4090/5090 24GB+; GGUF quantized version can try running on 12-16GB VRAM License: Check LTX official license Characteristics:

• High video quality, good motion fluidity.
• Supports multi-shot sequence generation.
• Native audio sync generation.
• Rich GGUF quantization versions; consumer GPUs can also experience it.
• Optional accelerated LoRA for further generation speed improvement. Suitable for: High-quality video generation research, multi-shot video workflows, audio-video sync generation

MiniMax-H3 ​

Parameters: Multimodal generation model Hardware requirements: Original recommends 16-24GB VRAM; quantized version runs basic functions on 8-12GB VRAM License: Check MiniMax official open-source agreement Characteristics:

• Supports video generation, audio-driven digital humans, reference-to-video and other modes.
• Native dual-channel audio generation, video + audio in one step.
• Supports reference image, reference video, reference audio multimodal control.
• Has specialized optimization for digital human generation.
• Quantization version lowers local running barriers. Suitable for: AI digital human systems, audio-video sync generation, reference-to-video, multimodal control research

4. Speech AI Direction ​

Speech direction graduation projects include: text-to-speech (TTS), automatic speech recognition (ASR), voice cloning, voice conversion, speech enhancement, etc. Moderate barriers, clear application scenarios.

Open-Source TTS Models ​

Representative models include IndexTTS, CosyVoice, GPT-SoVITS, etc.

Hardware requirements: RTX 3060 12GB sufficient; some models have lower VRAM requirements License: Various, mostly MIT/Apache/custom open-source licenses Characteristics:

• IndexTTS: Alibaba open-source, excellent sound quality and prosody, fast inference.
• CosyVoice: Tongyi Lab open-source, supports zero-shot voice cloning and multilingual.
• GPT-SoVITS: Community favorite, clones voice timbre with few samples, good Chinese support.
• Model files range from hundreds of MB to several GB; local deployment barriers are not high. Suitable for: AI dubbing systems, voice cloning applications, audiobook generation, multilingual speech synthesis, voice style conversion

Whisper (Speech Recognition) ​

Parameters: tiny / base / small / medium / large (39M to 1.5B) Hardware requirements: small version runs on CPU; large version recommends 8GB+ VRAM License: MIT Characteristics:

• OpenAI's open-source speech recognition model with high accuracy.
• Supports 90+ languages.
• Multiple model size tiers from ultra-lightweight to high-precision.
• Mature ecosystem; almost every speech project includes it. Suitable for: Speech transcription systems, subtitle generation, meeting records, multilingual speech recognition, voice assistants

5. Multimodal Direction ​

Multimodal is one of the most cutting-edge AI directions — models can simultaneously process text, images, audio, and other modalities. Multimodal graduation projects have high technical content, good demonstration effects, but also relatively greater difficulty.

MiniCPM-V / MiniCPM-o Series ​

Parameters: ~4-5B (multimodal model) Hardware requirements: ~8-12GB VRAM License: Apache 2.0 etc. Characteristics:

• Modelbest's open-source edge-side multimodal model, designed for lightweight from the start.
• Small model but strong capability, outstanding in its parameter class.
• Supports image-text understanding, OCR, visual Q&A.
• Has self-deployment code and complete documentation. Suitable for: Image Q&A systems, image-text understanding applications, OCR enhancement, multimodal dialogue, mobile AI applications

Qwen-VL / Qwen-Image-VL Series ​

Parameters: 2B / 7B / 14B and other tiers Hardware requirements: 7B version ~8-10GB VRAM after quantization License: Apache 2.0 Characteristics:

• Alibaba's open-source multimodal series with strong Chinese capability.
• Supports image understanding, OCR, document Q&A, chart understanding.
• Integrated with Qwen LLM ecosystem, complete toolchain. Suitable for: Document intelligent Q&A, chart analysis, image understanding applications, multimodal RAG

6. Application Development Direction ​

This direction is not about studying models themselves but using models to build application systems. High programming requirements, but the strongest "engineering feel" for graduation projects, and closest to actual work.

RAG (Retrieval-Augmented Generation) Systems ​

Core tech stack:

• LLM: Quantized models like Qwen / Llama / Mistral (choose tier by hardware; 12GB VRAM can use older versions like Qwen2.5 7B or small-activation MoE; 16GB+ can use Qwen3.8 27B).
• Embedding models: bge, m3e, text2vec, etc.
• Vector databases: ChromaDB, FAISS, Milvus.
• Frameworks: LangChain / LlamaIndex / Dify / MaxKB.

Hardware requirements: RTX 3060 12GB sufficient Suitable for:

• Professional domain knowledge base Q&A (legal/medical/financial/education).
• Enterprise intelligent customer service systems.
• Literature review assistants.
• Personal knowledge management AI assistants.

Why suitable for graduation projects: RAG is one of the hottest AI application directions — mature technology, abundant materials, can produce complete systems. And it can combine with different professional domains — finance students build financial research Q&A, law students build legal clause Q&A, medical students build medical knowledge Q&A; major + AI integration is high.

AI Agent Systems ​

Core tech stack:

• LLM: Same as above.
• Agent frameworks: LangGraph / CrewAI / AutoGen.
• Tool integration: Various APIs, MCP protocol.
• Frontend: Gradio / Streamlit.

Hardware requirements: Same as RAG Suitable for:

• Intelligent research assistants (auto-search, organize, write reports).
• Multi-Agent collaboration systems (division of labor for complex tasks).
• AI workflow automation tools.
• Domain-specific Agent applications.

Difficulty note: Agent concept is newer, debugging difficulty is higher than RAG; recommended for students with some programming foundation.

Image/Video Generation Application Systems ​

Core tech stack:

• Image models: FLUX.2 / Qwen-Image etc.
• Video models: Wan 2.2 5B etc.
• Frontend/workflow: ComfyUI API / custom web interface.

Hardware requirements: Image generation needs 8-12GB VRAM; video generation needs 16-24GB Suitable for:

• Industry-specific AI design tools (brand design, packaging design, architectural concept design, etc.).
• AI image editing workbench.
• Short video generation systems.
• Digital human driving applications.

Selection Advice: How to Choose the Right Model? ​

With so many models, how to choose? We offer a few practical suggestions:

1. Check Hardware First, Then Choose Models ​

Open Task Manager and check your GPU VRAM:

• 6-8GB (RTX 3050/4050/3060 6G): Choose quantized LLMs under 7B (older versions like Qwen2.5 7B still work), 4B image models, small TTS models.
• 12GB (RTX 3060/4060/5060 12GB): 7B quantized LLMs, FLUX.2 4B image, Wan 2.2 5B video, most TTS models.
• 16-24GB (RTX 4070/5070/4080/5080): Qwen3.8 27B quantized, FLUX.2 9B, better video models.
• 24GB+ (RTX 4090/5090/pro cards): Almost all open-source models can run, including fine-tuning.

Don't force large models. If 7B doesn't run, use 3B; if original doesn't run, use quantized. Graduation projects are about project completeness and depth of thought, not using the largest model.

2. Choose Models with Active Communities ​

Active communities mean: more tutorials, more troubleshooting Q&A, more tool support, guaranteed updates and maintenance. Models with high Hugging Face downloads, many GitHub stars, and many paper citations — prioritize.

3. Check the License ​

Generally not an issue for graduation projects, but if you later want to open-source or commercialize the project, pay attention to licenses:

• Apache 2.0 / MIT: Most permissive; use, modify, commercialize freely.
• GPL / AGPL: Copyleft open source; if you modify, you must also open-source.
• Custom commercial license: Read terms carefully; some are free for non-commercial use, paid for commercial.
• Research use only: Research only, no commercial use.

Common question: Does student graduation project count as educational, non-commercial use?

Our understanding: Graduation projects themselves are academic educational activities, non-commercial use, and almost all open-source licenses permit this. Whether Apache/MIT permissive licenses or "research use only" licenses, graduation projects fall within coverage. This is also open-source community consensus — academic research and teaching are important parts of the open-source spirit.

But note several boundaries:

• Commercializing graduation project results — e.g., using the project for entrepreneurship or product revenue after graduation — requires re-checking the license.
• Enterprise-sponsored or collaborative graduation projects — if the project has enterprise background and the enterprise will use results commercially, confirm license permissions in advance.
• Copyright of generated content — some models' training data sources are disputed; generated content may involve copyright issues. This is separate from the model license; pay attention independently.

In summary: Use freely for graduation projects; check licenses before commercializing.

4. Model Selection Is Just the First Step; the Workload Is in the Application Layer ​

We want to particularly remind: the core of a graduation project is not "what model was used" but "what was done with the model."

Both use Qwen for RAG — one person just sets up a demo running Q&A, another builds a complete domain knowledge base system with data preprocessing, retrieval optimization, evaluation comparison, user interface, and actual testing. The latter's workload and quality are clearly higher.

Models are tools; how to use tools to solve real problems is the focus of graduation projects.

Finally: About Local Deployment ​

For many students doing AI graduation projects, the first hurdle is not topic selection but can't set up the environment.

Models won't download, dependencies won't install, CUDA version is wrong, VRAM is insufficient... environment setup alone can take a week or two before the project even starts.

These problems all have solutions. Ollama makes local running of large models as simple as installing software; LM Studio provides a GUI for those who don't know command lines; ComfyUI turns image generation into a node-based workflow. There are also some all-in-one AI application deployment tools that support rapid deployment of common model versions (original, quantized, accelerated) — students don't need to spend time on environment setup and can start projects directly.

Our advice: minimize environment setup time and spend time on what really matters — understanding principles, designing systems, optimizing results, writing good papers. After all, graduation projects are about your thinking and implementation, not whether you can install environments.