Open-Source-LLMs sollen winzigen GPT-2-Nachfolger aufwerten

Project Background and the Core Question For our NLP group work, we built a Tiny GPT-2 from scratch: a decoder-only Transformer with a causal mask, trained to predict the next word in English text. It ran, but the problems were obvious — few parameters, limited training data, and outputs that were often repetitive or grammatically awkward.

My teacher's assignment was clear: research open-source LLMs and explain how they can improve my GPT-2 project. After doing the research, my conclusion is that the role of LLMs in this project is not to "replace" Tiny GPT-2, but to act as teacher models, data generators, and evaluation tools to make it better. Among these, Qwen3-4B-Instruct-2507 is the core model I ultimately chose, while the other LLMs each offer different kinds of supporting value.

What it is good at: This is an instruction-tuned model with significant improvements in instruction following, logical reasoning, text understanding, long-tail multilingual knowledge, and tool use. It runs only in "non-thinking mode," producing direct, structured output without generating intermediate reasoning blocks — which makes it ideal for tasks that need formatted output.

How it helps my GPT-2 project: This is the most important part. Tiny GPT-2 is a base model — it only continues text and does not "follow instructions." Qwen3-4B-Instruct is the opposite — it can generate content in a specified format. I can do three things: (1) Data augmentation: use Qwen3 to generate large amounts of high-quality English continuation samples and diverse sentences to expand my originally limited training corpus; (2) Evaluation judge: have Qwen3 score my Tiny GPT-2's next-word predictions for semantic plausibility, rather than only looking at perplexity; (3) if I want to further fine-tune Tiny GPT-2 for a specific continuation style, Qwen3 can also generate instruction-answer pairs to build training data.

Does it run on a free Colab T4 (16GB)? Yes. A 4B model in fp16 needs about 8GB of VRAM, leaving enough headroom on the T4's 16GB. In terms of speed, 4B inference on a T4 is not blazing fast, but it is entirely usable for batch-generating a few hundred training samples or evaluation examples.

GPT-2 Small's value is not in its "capability" but in its architectural alignment. My Tiny GPT-2 uses exactly the same components: the same tokenizer, the same causal language modeling objective, and the same attention mechanism. This means I can do knowledge distillation: instead of learning only "what the correct next word is" (hard labels), my small model learns GPT-2 Small's probability distribution over the entire vocabulary (soft labels). Because the architectures align, there is no vocabulary or structural mismatch between teacher and student — the soft targets transfer directly. This is the most direct and effective improvement I can make without changing a single line of my model code.

SmolLM3's unique advantage is that its training recipe is fully open. I can inspect what data, hyperparameters, and tokenization strategy it used, then compare them against my own Tiny GPT-2 training setup and identify what I can borrow. At 3B parameters, it needs about 6GB in fp16 — easily accommodated on a T4. For a learning-oriented project, this transparency is more valuable than raw capability alone.

Gemma 3 4B (Google): 4B parameters, 128K context window, supports 140+ languages. Requires acceptance of Google's terms of use. Its strengths are multilingual support and long context, but my project is English-only, so these extra capabilities are not useful to me.

Phi-4-mini-instruct (Microsoft): 3.8B parameters, MIT license. It performs well on reasoning benchmarks, but it is positioned as a "small reasoning model" — for my next-word prediction task, its instruction-following ability is not as directly useful as Qwen3's.

Mistral 7B Instruct (Mistral AI): 7B parameters, 32K context. More capable but larger; 7B in fp16 needs about 14GB, leaving almost no headroom on a T4 for other operations, so it is not suitable for my scenario.

Comparison Table | Model | Developer | Parameters | Context Window | License | Runs on T4? | API key / account needed? | Core value for my project | |---|---|---|---|---|---|---|---| | Qwen3-4B-Instruct-2507 | Alibaba | 4B | 262K | Apache 2.0 | Yes (~8GB) | No | Data generation + semantic evaluation judge | | GPT-2 Small | OpenAI | 124M | 1K | MIT | Yes (<1GB) | No | Distillation teacher (architecture aligned) | | SmolLM3-3B | Hugging Face | 3B | 64K–128K | Apache 2.0 | Yes (~6GB) | No | Reproducible training recipe reference | | Llama 3.2-3B-Instruct | Meta | 3.2B | 128K | Llama 3.2 | Yes (~6.4GB) | HF account + terms acceptance | Backup data generator | | Gemma 3 4B | Google | 4B | 128K | Gemma license | Yes (~8GB) | Terms acceptance | Only advantageous for multilingual | | Phi-4-mini-instruct | Microsoft | 3.8B | 128K | MIT | Yes (~7.6GB) | No | Reasoning tasks, not my focus | The Model I Ultimately Chose: Qwen3-4B-Instruct-2507 Reasons for the choice (evidence-based):

First, it directly addresses Tiny GPT-2's most fundamental weakness — data. One of the core reasons my small model's prediction quality is limited is that its training corpus is not rich enough. Qwen3-4B-Instruct's instruction-following ability lets me batch-generate diverse English sentences and continuation samples with simple prompts, expanding a corpus of a few hundred examples into several thousand. This is not "replacement training" — it is "supplementary training."

Second, it provides an evaluation dimension that Tiny GPT-2 cannot perform on itself. Perplexity can only tell me "how surprised the model is by the next word," but it cannot judge "whether that word is reasonable in context." Qwen3, as a judge, can assign a semantic plausibility score to each prediction, letting me know whether the model is genuinely learning language or merely memorizing high-frequency collocations.

Third, zero engineering friction. The Apache 2.0 license means I do not need to register an account, accept any terms, or use an API key. A single from_pretrained line in transformers loads it, 8GB of VRAM lets it run on a free T4, and there is still headroom for batch inference.

Why not use Qwen3 directly as the distillation teacher? The capacity gap between 4B and Tiny GPT-2 is too large. Directly distilling a 4B model into a small model with a few million parameters often hurts performance rather than helping it. GPT-2 Small (124M) is the teacher whose scale matches my Tiny GPT-2. So my strategy is: Qwen3-4B handles "data and evaluation," while GPT-2 Small handles "distillation" — each with a clear division of labor.

Conclusion Tiny GPT-2 is a great exercise for understanding LLM internals — you can see every token, every mask, every softmax. But it does not need to exist in isolation. By using Qwen3-4B-Instruct to generate training data and evaluate prediction quality, and GPT-2 Small for architecture-aligned distillation, my next-word prediction project can go from "it runs" to "it has measurable improvements." That is the greatest help open-source LLMs can offer a small learning project: not doing your homework for you, but making your homework better.

References Qwen Team. Qwen3-4B-Instruct-2507 model card (ModelScope / Hugging Face), Apache 2.0 license, 262K native context.

Hugging Face. SmolLM3-3B model card, Apache 2.0, 64K training context, YaRN-extendable to 128K.

Google AI. Gemma 3 model card, 128K context (4B/12B/27B versions).

NVIDIA / Meta. Llama 3.2-3B-Instruct documentation and benchmarks.

Microsoft / Hugging Face. Phi-4-mini-instruct model card, MIT license, 3.8B parameters.

AWS / Mistral AI. Mistral 7B Instruct model card, 32K context.

OpenAI. GPT-2 model card (124M, 1K context, MIT).