Update README.md

README.md

@@ -9,4 +9,99 @@ library_name: transformers
 tags:
 - Non-Reasoning
 - text-generation-inference
----
+datasets:
+- prithivMLmods/Nemotron-Safety-30K
+---
+
+![89.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/HVL6VpZK94O7cHg-u66ao.png)
+
+# **Computron-Bots-1.7B-R1**
+
+> **Computron-Bots-1.7B-R1** is a **general-purpose safe question-answering model** fine-tuned from **Qwen3-1.7B**, specifically designed for **direct and efficient factual responses** without complex reasoning chains. It provides straightforward, accurate answers across diverse topics, making it ideal for knowledge retrieval, information systems, and applications requiring quick, reliable responses.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF](https://huggingface.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF)
+
+
+## **Key Features**
+1. **Direct Question Answering Excellence**  
+   Trained to provide clear, concise, and accurate answers to factual questions across a wide range of topics without unnecessary elaboration or complex reasoning steps.
+
+2. **General-Purpose Knowledge Base**  
+   Capable of handling diverse question types including factual queries, definitions, explanations, and general knowledge questions with consistent reliability.
+
+3. **Efficient Non-Reasoning Architecture**  
+   Optimized for fast, direct responses without step-by-step reasoning processes, making it perfect for applications requiring immediate answers and high throughput.
+
+4. **Compact yet Knowledgeable**  
+   Despite its 1.7B parameter size, delivers strong performance for factual accuracy and knowledge retrieval with minimal computational overhead.
+
+## **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Computron-Bots-1.7B-R1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "What is the capital of France?"
+
+messages = [
+    {"role": "system", "content": "You are a knowledgeable assistant that provides direct, accurate answers to questions."},
+    {"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=256,
+    temperature=0.7,
+    do_sample=True
+)
+
+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**
+- **Knowledge Base Systems**: Quick factual retrieval for databases and information systems.
+- **Educational Tools**: Direct answers for students and learners seeking factual information.
+- **Customer Support Bots**: Efficient responses to common questions and inquiries.
+- **Search Enhancement**: Improving search results with direct, relevant answers.
+- **API Integration**: Lightweight question-answering service for applications and websites.
+- **Research Assistance**: Quick fact-checking and information gathering for researchers.
+
+## **Limitations**
+1. **Non-Reasoning Architecture**:  
+   Designed for direct answers rather than complex reasoning, problem-solving, or multi-step analysis tasks.
+
+2. **Limited Creative Tasks**:  
+   Not optimized for creative writing, storytelling, or tasks requiring imagination and artistic expression.
+
+3. **Context Dependency**:  
+   May struggle with questions requiring extensive context or nuanced understanding of complex scenarios.
+
+4. **Parameter Scale Constraints**:  
+   The 1.7B parameter size may limit performance on highly specialized or technical domains compared to larger models.
+
+5. **Base Model Limitations**:  
+   Inherits any limitations from Qwen3-1.7B's training data and may reflect biases present in the base model.
+
+6. **Conversational Depth**:  
+   While excellent for Q&A, may not provide the depth of engagement expected in extended conversational scenarios.