| --- |
| 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}, |
| } |
| ``` |
