115 lines
3.7 KiB
Markdown
115 lines
3.7 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- code
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- general-reasoning
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- moe
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- math
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---
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# **Lynx-TinySync-0.6B**
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> **Lynx-TinySync-0.6B** is a lightweight, high-performance model designed for **mathematical reasoning**, **code generation**, and **general-purpose inference**. Built on a custom modular dataset and powered by an efficient architecture, it excels in delivering structured, accurate outputs even in mid-resource environments. Despite its compact **0.6B parameter size**, it demonstrates remarkable proficiency in math, code, and technical language understanding.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Lynx-TinySync-0.6B-GGUF](https://huggingface.co/prithivMLmods/Lynx-TinySync-0.6B-GGUF)
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---
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## **Key Features**
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1. **Custom Modular Dataset Training**
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Fine-tuned using a handcrafted blend of math, code, and reasoning datasets, ensuring high performance in symbolic tasks and general queries.
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2. **Mathematical Reasoning**
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Handles algebra, calculus, geometry, and symbolic logic with clarity—ideal for tutoring, educational support, and math competitions.
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3. **Compact Code Assistant**
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Generates clean, efficient code in Python, JavaScript, and more—complete with explanations and bug-fix breakdowns.
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4. **Structured Output Generation**
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Outputs in JSON, Markdown, LaTeX, and tabular formats—well-suited for documentation, structured data templates, and technical content.
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5. **Multilingual Technical Reasoning**
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Supports math and code queries in 20+ languages with consistent output—enabling multilingual academic and professional use cases.
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6. **Optimized for Low-Resource Deployment**
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With only 0.6B parameters, it's ideal for inference on edge devices, local machines, and GPU-constrained environments.
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---
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Lynx-TinySync-0.6B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."
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messages = [
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{"role": "system", "content": "You are a step-by-step math tutor."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## **Intended Use**
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* Mathematical problem solving and symbolic logic
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* Lightweight code generation and debugging
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* Generation of structured content (e.g., JSON, LaTeX, Markdown)
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* Educational support across languages and domains
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* Low-resource deployment in academic or field settings
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---
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## **Limitations**
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* May underperform on long-form creative generation tasks
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* Smaller context window may limit deep multi-turn reasoning
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* Less capable in adversarial or abstract reasoning queries
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* Technical multilingual use focused—general dialogue fluency limited
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---
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## **References**
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1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071) |