---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Qwen3-1.7B-ft-bf16
pipeline_tag: text-generation
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.