初始化项目，由ModelHub XC社区提供模型

Model: SeranVishwa/master-buddy-v1.0
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- text-generation
+- causal-lm
+- conversational
+- llama
+- dpo
+- safetensors
+- study-assistant
+---
+
+# Model Card for Master Buddy AI v1.0
+
+Master Buddy AI is a fine-tuned conversational language model designed as a computer science and engineering study assistant. It is built to help users understand programming concepts, algorithms, and general technical topics in a structured and helpful manner.
+
+---
+
+## Model Details
+
+### Model Description
+
+Master Buddy AI v1.0 is a decoder-only transformer-based language model fine-tuned using:
+
+- Supervised Fine-Tuning (SFT)
+- Direct Preference Optimization (DPO)
+
+The goal of this model is to improve response helpfulness, reasoning ability, and instruction-following quality.
+
+- **Developed by:** Seran Vishwa  
+- **Model type:** Causal Language Model (Decoder-only Transformer)  
+- **Language(s):** English  
+- **License:** Apache-2.0  
+- **Finetuned from model:** LLaMA-based architecture or equivalent base model
+
+---
+
+## Model Sources
+
+- **Repository:** https://huggingface.co/SeranVishwa/master-buddy-v1.0  
+- **Demo:** Add deployment link when available
+
+---
+
+## Uses
+
+### Direct Use
+
+This model is intended for:
+
+- Programming assistance
+- Data structures and algorithms explanations
+- Computer science concepts (OS, DBMS, Networks)
+- Educational and study support
+- General question answering
+
+---
+
+### Downstream Use
+
+The model can be integrated into:
+
+- AI tutoring systems
+- Chatbot applications
+- Educational platforms
+- Developer tools
+- Web-based AI assistants
+
+---
+
+### Out-of-Scope Use
+
+This model is not intended for:
+
+- Medical advice
+- Legal advice
+- Financial decision-making
+- Safety-critical systems
+- Harmful or malicious use cases
+
+---
+
+## Bias, Risks, and Limitations
+
+- The model may produce incorrect or hallucinated responses
+- It reflects biases present in training data
+- It should not be used as a sole source of truth
+- Outputs should be verified for critical use cases
+
+---
+
+### Recommendations
+
+Users should:
+
+- Verify important technical outputs
+- Use the model as a learning assistant, not an authority
+- Be aware of possible reasoning errors
+
+---
+
+## How to Get Started
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("SeranVishwa/master-buddy-v1.0")
+tokenizer = AutoTokenizer.from_pretrained("SeranVishwa/master-buddy-v1.0")
+
+prompt = "Explain Dijkstra's algorithm"
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))