97 lines
3.8 KiB
Markdown
97 lines
3.8 KiB
Markdown
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---
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library_name: transformers
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tags:
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- text-generation-inference
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- Math
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- Code
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- Thinker
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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---
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# **Gamma-Velorum-1.5B-Thinker**
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> **Gamma-Velorum-1.5B-Thinker** is a **math and code reasoning model** fine-tuned from **Qwen2.5-1.5B**, crafted to tackle complex **mathematical** and **programming** problems using **chain-of-thought** methodology. It excels in **step-by-step explanations**, long-context understanding, and bilingual support — ideal for education, coding tutors, and logic-intensive applications.
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## **Key Features**
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1. **Math + Code Chain-of-Thought Reasoning**
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Trained to provide detailed, structured steps for both **mathematical** and **coding** problems. Gamma-Velorum-1.5B-Thinker explains not just the what, but the *why*, ensuring clarity in logic and computation.
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2. **Backed by Qwen2.5-1.5B**
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Built on the latest Qwen2.5 architecture, bringing improved accuracy, reasoning capabilities, and enhanced tokenizer efficiency.
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3. **Long-Context Problem Solving**
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Capable of handling **long multi-turn questions**, nested logic, and extended code/math scenarios — ideal for competitive exams or coding challenges.
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4. **Bilingual (English + Chinese)**
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Seamlessly understands and reasons through prompts in both **English** and **Simplified Chinese**, making it versatile for global education platforms.
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5. **Efficient and Lightweight**
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With only 1.5B parameters, it strikes a balance between **performance and deployability**, suitable for web, edge, and mobile environments.
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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/Gamma-Velorum-1.5B-Thinker"
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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 = "Write a Python function to calculate factorial of a number."
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messages = [
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{"role": "system", "content": "You are a helpful tutor skilled in math and programming. Explain solutions step-by-step."},
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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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```
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## **Intended Use**
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- **Math & Coding Tutors**: Solves word problems, algebra, logic puzzles, and programming challenges with clarity and precision.
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- **Bilingual EdTech Apps**: Explains both math and code in English and Chinese for a broader learning reach.
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- **STEM Reasoning Engines**: Powers scientific reasoning tools, code-assist bots, and step-by-step logic solvers.
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- **Lightweight LLM Use Cases**: Browser-based, embedded systems, or mobile apps for learners and developers.
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## **Limitations**
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1. **Domain Focused**:
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Optimized for **STEM and code** tasks — general conversation or abstract creative writing may not be as strong.
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2. **Scale Limitations**:
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As a 1.5B parameter model, it may not match larger models on highly complex logic or long-form generation.
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3. **Bias Inheritance**:
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Carries forward biases from its Qwen2.5 base model — important for sensitive contexts.
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4. **Prompt Structuring Matters**:
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Performs best with explicit, structured prompts for math/code. Ambiguous or casual phrasing may reduce accuracy.
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