初始化项目，由ModelHub XC社区提供模型

Model: Surpem/Supertron1-4B
Source: Original Platform

README.md

@@ -0,0 +1,81 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- reasoning
+- math
+- coding
+- instruction-tuned
+- pytorch
+---
+
+# **Supertron1-4B: A Capable, Efficient Instruction-Tuned Language Model**
+
+## **Model Description**
+
+**Supertron1-4B** is an instruction-tuned language model built on top of Qwen3-4B. Designed to be a **reliable, efficient daily driver**, it delivers strong performance across math, coding, reasoning, and general conversation while remaining fast and lightweight enough to run on consumer hardware.
+
+* **Developed by:** Surpem
+* **Model type:** Causal Language Model
+* **Architecture:** Dense Transformer, 4B parameters
+* **Fine-tuned from:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
+* **License:** Apache 2.0
+
+---
+
+## **Results**
+
+Supertron1-4B holds its own against models in the 4–8B class and surpasses Mistral 7B on all four core benchmarks despite having nearly half the parameters.
+
+<div align="center">
+<p align="center"><a href="https://postimg.cc/zLfkBN3D"><img width=800 src="https://i.postimg.cc/0NYXV2sS/1000037258.png"/></a></p>
+</div>
+
+**Key takeaways:**
+- Beats Mistral 7B on every benchmark at 4B parameters
+- Strong GSM8K and HumanEval performance from math and coding focused tuning
+- Competitive with Phi-4 mini on a fraction of the compute
+
+---
+
+## **Get Started**
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "surpem/supertron1-4b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Explain the difference between LoRA and full fine-tuning."}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+## **Citation**
+
+```bibtex
+@misc{surpem2026supertron1,
+      title={Supertron1-4B — Efficient Instruction-Tuned Language Model},
+      author={Surpem},
+      year={2026},
+      url={https://huggingface.co/surpem/supertron1-4b},
+}
+```