3.7 KiB
license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
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
Key Features
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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.
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Mathematical Reasoning Handles algebra, calculus, geometry, and symbolic logic with clarity—ideal for tutoring, educational support, and math competitions.
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Compact Code Assistant Generates clean, efficient code in Python, JavaScript, and more—complete with explanations and bug-fix breakdowns.
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Structured Output Generation Outputs in JSON, Markdown, LaTeX, and tabular formats—well-suited for documentation, structured data templates, and technical content.
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Multilingual Technical Reasoning Supports math and code queries in 20+ languages with consistent output—enabling multilingual academic and professional use cases.
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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
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