Instructions to use SyedCode01/rhythmai-cybersec-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SyedCode01/rhythmai-cybersec-20b with Transformers:

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

pipe = pipeline("text-generation", model="SyedCode01/rhythmai-cybersec-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SyedCode01/rhythmai-cybersec-20b", dtype="auto")

llama-cpp-python

How to use SyedCode01/rhythmai-cybersec-20b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SyedCode01/rhythmai-cybersec-20b",
	filename="gguf-q4_k_m_gguf/gpt-oss-20b.MXFP4.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 SyedCode01/rhythmai-cybersec-20b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
# Run inference directly in the terminal:
llama-cli -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
# Run inference directly in the terminal:
llama-cli -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

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 SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
# Run inference directly in the terminal:
./llama-cli -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

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 SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

Use Docker

docker model run hf.co/SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

LM Studio
Jan

vLLM

How to use SyedCode01/rhythmai-cybersec-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SyedCode01/rhythmai-cybersec-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SyedCode01/rhythmai-cybersec-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

SGLang

How to use SyedCode01/rhythmai-cybersec-20b 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 "SyedCode01/rhythmai-cybersec-20b" \
    --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": "SyedCode01/rhythmai-cybersec-20b",
		"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 "SyedCode01/rhythmai-cybersec-20b" \
        --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": "SyedCode01/rhythmai-cybersec-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SyedCode01/rhythmai-cybersec-20b with Ollama:
```
ollama run hf.co/SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
```

Unsloth Studio new

How to use SyedCode01/rhythmai-cybersec-20b 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 SyedCode01/rhythmai-cybersec-20b 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 SyedCode01/rhythmai-cybersec-20b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SyedCode01/rhythmai-cybersec-20b to start chatting

Pi new

How to use SyedCode01/rhythmai-cybersec-20b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

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": "SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SyedCode01/rhythmai-cybersec-20b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

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 SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

Run Hermes

hermes

Docker Model Runner
How to use SyedCode01/rhythmai-cybersec-20b with Docker Model Runner:
```
docker model run hf.co/SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF
```

Lemonade

How to use SyedCode01/rhythmai-cybersec-20b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SyedCode01/rhythmai-cybersec-20b:Q4_K_M_GGUF

Run and chat with the model

lemonade run user.rhythmai-cybersec-20b-Q4_K_M_GGUF

List all available models

lemonade list

SyedCode01 commited on Mar 6

Commit

a56dd59

verified ·

1 Parent(s): 8bf6b0b

Upload README.md with huggingface_hub

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README.md CHANGED Viewed

@@ -54,7 +54,7 @@ Built for [RhythmAI](https://github.com/SyedCode01) -- an AI-powered SOC platfor
 | **LoRA Dropout** | 0.05 |
 | **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
 | **Training Precision** | 4-bit QLoRA with BF16 compute |
-| **Context Length** | 4,096 tokens |
 | **GGUF Format** | MXFP4 (13 GB) |
 | **License** | Apache 2.0 (inherited from GPT-OSS) |
@@ -192,7 +192,7 @@ This model is designed for cybersecurity professionals, SOC analysts, and securi
 - **Domain-specific**: Optimized for cybersecurity tasks; general knowledge may be less reliable than the base model
 - **Not a replacement for human analysts**: Outputs should be validated by qualified security professionals
 - **Training data bias**: Performance may vary for threats or attack patterns not well-represented in the training data
-- **Context window**: Limited to 4,096 tokens; very long log files or reports may need to be chunked
 - **No real-time data**: The model does not have access to real-time threat intelligence feeds
 ## Citation
@@ -200,7 +200,7 @@ This model is designed for cybersecurity professionals, SOC analysts, and securi
 ```bibtex
 @misc{rhythmai-cybersec-20b,
   title={RhythmAI Cybersec 20B: A Fine-Tuned Cybersecurity Language Model},
-  author={Syed Hasan Rizvi},
   year={2026},
   url={https://huggingface.co/SyedCode01/rhythmai-cybersec-20b},
   note={Fine-tuned from OpenAI GPT-OSS-20B for SOC operations}

 | **LoRA Dropout** | 0.05 |
 | **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
 | **Training Precision** | 4-bit QLoRA with BF16 compute |
+| **Context Length** | 65,536 tokens (64K) |
 | **GGUF Format** | MXFP4 (13 GB) |
 | **License** | Apache 2.0 (inherited from GPT-OSS) |
 - **Domain-specific**: Optimized for cybersecurity tasks; general knowledge may be less reliable than the base model
 - **Not a replacement for human analysts**: Outputs should be validated by qualified security professionals
 - **Training data bias**: Performance may vary for threats or attack patterns not well-represented in the training data
+- **Context window**: Supports up to 65,536 tokens (64K); training used 4,096 max sequence length but the base model's full context capability is preserved
 - **No real-time data**: The model does not have access to real-time threat intelligence feeds
 ## Citation
 ```bibtex
 @misc{rhythmai-cybersec-20b,
   title={RhythmAI Cybersec 20B: A Fine-Tuned Cybersecurity Language Model},
+  author={Syed Hasan Iqbal},
   year={2026},
   url={https://huggingface.co/SyedCode01/rhythmai-cybersec-20b},
   note={Fine-tuned from OpenAI GPT-OSS-20B for SOC operations}