Instructions to use saishshinde15/Clyrai_Base_Reasoning_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saishshinde15/Clyrai_Base_Reasoning_GGUF", dtype="auto")

llama-cpp-python

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saishshinde15/Clyrai_Base_Reasoning_GGUF",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf saishshinde15/Clyrai_Base_Reasoning_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 saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf saishshinde15/Clyrai_Base_Reasoning_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 saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saishshinde15/Clyrai_Base_Reasoning_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 saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M

Use Docker

docker model run hf.co/saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Ollama:
```
ollama run hf.co/saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
```

Unsloth Studio

How to use saishshinde15/Clyrai_Base_Reasoning_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 saishshinde15/Clyrai_Base_Reasoning_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 saishshinde15/Clyrai_Base_Reasoning_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saishshinde15/Clyrai_Base_Reasoning_GGUF to start chatting

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saishshinde15/Clyrai_Base_Reasoning_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": "saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Docker Model Runner:
```
docker model run hf.co/saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M
```

Lemonade

How to use saishshinde15/Clyrai_Base_Reasoning_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saishshinde15/Clyrai_Base_Reasoning_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Clyrai_Base_Reasoning_GGUF-Q4_K_M

List all available models

lemonade list

saishshinde15/Clyrai_Base_Reasoning_GGUF (GGUF - Q4) (Formerly known as TBH.AI Base Reasoning )

Developed by: Clyrai
License: apache-2.0
Fine-tuned from: Qwen/Qwen2.5-3B-Instruct
GGUF Format: 4-bit quantized (Q4) for optimized inference

Model Description

Clyrai Base Reasoning (GGUF - Q4) is a 4-bit GGUF quantized version of saishshinde15/Clyrai_Base_Reasoning, a fine-tuned model based on Qwen 2.5. This version is designed for high-efficiency inference on CPU/GPU with minimal memory usage, making it ideal for on-device applications and low-latency AI systems.

Trained using GRPO (General Reinforcement with Policy Optimization), the model excels in self-reasoning, logical deduction, and structured problem-solving, comparable to DeepSeek-R1. The Q4 quantization ensures significantly lower memory requirements while maintaining strong reasoning performance.

Features

4-bit Quantization (Q4 GGUF): Optimized for low-memory, high-speed inference on compatible backends.
Self-Reasoning AI: Can process complex queries autonomously, generating logical and structured responses.
GRPO Fine-Tuning: Uses policy optimization for improved logical consistency and step-by-step reasoning.
Efficient On-Device Deployment: Works seamlessly with llama.cpp, KoboldCpp, GPT4All, and ctransformers.
Ideal for Logical Tasks: Best suited for research, coding logic, structured Q&A, and decision-making applications.

Limitations

This Q4 GGUF version is inference-only and does not support additional fine-tuning.
Quantization may slightly reduce response accuracy compared to FP16/full-precision models.
Performance depends on the execution environment and GGUF-compatible runtime.

Usage

Use this prompt for more detailed and personalized results. This is the recommended prompt as the model was tuned on it.

You are a reasoning model made by researcher at Clyrai and your role is to respond in the following format only and in detail :

<reasoning>
...
</reasoning>
<answer>
...
</answer>

Use this prompt for concise representation of answers.

SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

Downloads last month: 31

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for saishshinde15/Clyrai_Base_Reasoning_GGUF

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