初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/GCIRS-Reasoning-1.5B-R1
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+tags:
+- reinforcement-learning
+- text-generation-inference
+- science
+- code
+- math
+- finance
+pipeline_tag: text-generation
+---
+
+![R1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BKHWttLe9Z8hJ-azW0b8i.png)
+
+# **GCIRS-Reasoning-1.5B-R1**
+
+> **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.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF](https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Reinforcement Learning on Big Reasoning Traces**
+   Fine-tuned using **DeepSeek’s Big Reasoning Traces**, ensuring clarity in multi-step reasoning, factual deduction, and long-form scientific argumentation.
+
+2. **Research-Ready Scientific Fidelity**
+   Designed for researchers, educators, and analysts—offers **reliable factual recall**, **logical structuring**, and precise step-by-step explanation.
+
+3. **Structured Output in LaTeX, Markdown, and JSON**
+   Supports technical documentation and publishing with seamless integration of **LaTeX equations**, **Markdown formatting**, and **JSON output**.
+
+4. **Multilingual Technical Reasoning**
+   Effective across **20+ languages**, especially in **scientific**, **academic**, and **technical domains**.
+
+5. **Efficient for Inference**
+   Despite its **1.5B parameter scale**, it's optimized for **low-latency inference** across **modern GPUs** and **research pipelines**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/GCIRS-Reasoning-1.5B-R1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the principle of entropy in thermodynamics with examples."
+
+messages = [
+    {"role": "system", "content": "You are a scientific reasoning assistant."},
+    {"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**
+
+* Scientific and research-grade question answering
+* Conceptual explanations in physics, biology, and chemistry
+* Factual, non-fictional structured content generation
+* Academic tutoring and reasoning assessment
+* High-fidelity inference in low-latency research settings
+
+## **Limitations**
+
+* Not designed for casual chat or storytelling
+* Performance may decline outside scientific/technical domains
+* Limited creativity and abstract generalization
+* Context limitations in extremely long research documents
+
+## **References**
+
+1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
+2. [Big Reasoning Traces (DeepSeek Research)]()
+3. [Reinforcement Learning with Human Feedback (RLHF)](https://arxiv.org/abs/1906.01749)