Instructions to use prithivMLmods/Computron-Bots-1.7B-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Computron-Bots-1.7B-R1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Computron-Bots-1.7B-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Computron-Bots-1.7B-R1")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Computron-Bots-1.7B-R1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Computron-Bots-1.7B-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Computron-Bots-1.7B-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Computron-Bots-1.7B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Computron-Bots-1.7B-R1

SGLang

How to use prithivMLmods/Computron-Bots-1.7B-R1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Computron-Bots-1.7B-R1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Computron-Bots-1.7B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Computron-Bots-1.7B-R1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Computron-Bots-1.7B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Computron-Bots-1.7B-R1 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Computron-Bots-1.7B-R1
```

Computron-Bots-1.7B-R1 / README.md

prithivMLmods

Update README.md

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	---
	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.