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