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Model: Surpem/Supertron2-1.7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- reasoning
+- math
+- coding
+- instruction-tuned
+- pytorch
+---
+# **Supertron2-1.7B: A Compact, Efficient Instruction-Tuned Language Model**
+## **Model Description**
+**Supertron2-1.7B** is an instruction-tuned language model built on top of Qwen3-1.7B. Designed to be a **reliable, efficient daily driver**, it delivers strong performance across math, coding, reasoning, science, general knowledge, and general conversation while remaining lightweight enough to run on consumer hardware.
+
+* **Developed by:** Surpem
+* **Model type:** Causal Language Model
+* **Architecture:** Dense Transformer, 1.7B parameters
+* **Fine-tuned from:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+* **License:** Apache 2.0
+
+---
+
+## **Capabilities**
+
+### **Reasoning**
+Supertron2-1.7B is designed for clear multi-step reasoning, making it capable of breaking down complex problems in a structured and useful way. It can work through questions methodically rather than jumping directly to a final answer.
+
+### **Math**
+The model handles a range of math tasks, from arithmetic and algebra to word problems and structured problem solving. It is useful for explaining steps, checking calculations, and producing concise final answers.
+
+### **Coding**
+Supertron2-1.7B can write, debug, and explain code across popular languages including Python, JavaScript, C++, and more. It understands syntax, common programming patterns, algorithmic reasoning, and practical implementation details.
+
+### **Science & General Knowledge**
+Broad instruction tuning across science, STEM, and general knowledge domains means the model can hold technical conversations, explain difficult concepts clearly, and assist with research, writing, and analysis tasks.
+
+### **Instruction Following**
+The model is responsive to natural language instructions. Whether you need concise answers, detailed explanations, structured output, or creative writing, Supertron2-1.7B adapts to the format and tone you ask for without needing complex prompting tricks.
+
+---
+
+## **Get Started**
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "Surpem/Supertron2-1.7B"
+
+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))
+```
+
+---
+
+## **Hardware Requirements**
+| Precision | Min VRAM | Recommended |
+|---|---|---|
+| bfloat16 | 5 GB | 8 GB+ |
+| 4-bit quantized | 3 GB | 4 GB+ |
+
+For 4-bit quantized inference:
+```python
+from transformers import BitsAndBytesConfig
+
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
+```
+
+---
+
+## **Citation**
+```bibtex
+@misc{surpem2026supertron2-1.7b,
+      title={Supertron2-1.7B — Efficient Instruction-Tuned Language Model},
+      author={Surpem},
+      year={2026},
+      url={https://huggingface.co/Surpem/Supertron2-1.7B},
+}
+```