110 lines
3.6 KiB
Markdown
110 lines
3.6 KiB
Markdown
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---
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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/Qwen2.5-1.5B-Instruct
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library_name: transformers
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tags:
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- reinforcement-learning
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- text-generation-inference
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- science
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- code
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- math
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- finance
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pipeline_tag: text-generation
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---
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# **GCIRS-Reasoning-1.5B-R1**
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> **GCIRS-Reasoning-1.5B-R1** is a **research-grade reasoning model** fine-tuned from **Qwen2.5-1.5B-Instruct**, focused on **non-fictional reasoning**, **factual consistency**, and **scientific depth**. Trained with reinforcement learning using the **Big Reasoning Traces** dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF](https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF)
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---
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## **Key Features**
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1. **Reinforcement Learning on Big Reasoning Traces**
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Fine-tuned using **DeepSeek’s Big Reasoning Traces**, ensuring clarity in multi-step reasoning, factual deduction, and long-form scientific argumentation.
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2. **Research-Ready Scientific Fidelity**
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Designed for researchers, educators, and analysts—offers **reliable factual recall**, **logical structuring**, and precise step-by-step explanation.
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3. **Structured Output in LaTeX, Markdown, and JSON**
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Supports technical documentation and publishing with seamless integration of **LaTeX equations**, **Markdown formatting**, and **JSON output**.
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4. **Multilingual Technical Reasoning**
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Effective across **20+ languages**, especially in **scientific**, **academic**, and **technical domains**.
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5. **Efficient for Inference**
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Despite its **1.5B parameter scale**, it's optimized for **low-latency inference** across **modern GPUs** and **research pipelines**.
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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/GCIRS-Reasoning-1.5B-R1"
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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 = "Explain the principle of entropy in thermodynamics with examples."
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messages = [
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{"role": "system", "content": "You are a scientific reasoning assistant."},
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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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* Scientific and research-grade question answering
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* Conceptual explanations in physics, biology, and chemistry
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* Factual, non-fictional structured content generation
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* Academic tutoring and reasoning assessment
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* High-fidelity inference in low-latency research settings
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## **Limitations**
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* Not designed for casual chat or storytelling
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* Performance may decline outside scientific/technical domains
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* Limited creativity and abstract generalization
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* Context limitations in extremely long research documents
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## **References**
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1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
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2. [Big Reasoning Traces (DeepSeek Research)]()
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3. [Reinforcement Learning with Human Feedback (RLHF)](https://arxiv.org/abs/1906.01749)
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