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

Libraries

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF 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-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Computron-Bots-1.7B-R1-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Computron-Bots-1.7B-R1-GGUF",
	filename="Computron-Bots-1.7B-R1.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF 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-GGUF"
# 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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF 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-GGUF" \
    --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-GGUF",
		"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-GGUF" \
        --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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Computron-Bots-1.7B-R1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Computron-Bots-1.7B-R1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Computron-Bots-1.7B-R1-GGUF to start chatting

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Computron-Bots-1.7B-R1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/Computron-Bots-1.7B-R1-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Computron-Bots-1.7B-R1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Computron-Bots-1.7B-R1-GGUF-Q4_K_M

List all available models

lemonade list

Computron-Bots-1.7B-R1-GGUF

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.

Model Files

File Name	Size	Format	Description
Computron-Bots-1.7B-R1.F32.gguf	6.89 GB	F32	Full precision 32-bit floating point
Computron-Bots-1.7B-R1.F16.gguf	3.45 GB	F16	Half precision 16-bit floating point
Computron-Bots-1.7B-R1.BF16.gguf	3.45 GB	BF16	Brain floating point 16-bit
Computron-Bots-1.7B-R1.Q8_0.gguf	1.83 GB	Q8_0	8-bit quantized
Computron-Bots-1.7B-R1.Q6_K.gguf	1.42 GB	Q6_K	6-bit quantized
Computron-Bots-1.7B-R1.Q5_K_M.gguf	1.26 GB	Q5_K_M	5-bit quantized, medium quality
Computron-Bots-1.7B-R1.Q5_K_S.gguf	1.23 GB	Q5_K_S	5-bit quantized, small quality
Computron-Bots-1.7B-R1.Q4_K_M.gguf	1.11 GB	Q4_K_M	4-bit quantized, medium quality
Computron-Bots-1.7B-R1.Q4_K_S.gguf	1.06 GB	Q4_K_S	4-bit quantized, small quality
Computron-Bots-1.7B-R1.Q3_K_L.gguf	1 GB	Q3_K_L	3-bit quantized, large quality
Computron-Bots-1.7B-R1.Q3_K_M.gguf	940 MB	Q3_K_M	3-bit quantized, medium quality
Computron-Bots-1.7B-R1.Q3_K_S.gguf	867 MB	Q3_K_S	3-bit quantized, small quality
Computron-Bots-1.7B-R1.Q2_K.gguf	778 MB	Q2_K	2-bit quantized