Instructions to use Tesleum/shirdel-coder-9b-claude-fable-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tesleum/shirdel-coder-9b-claude-fable-5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tesleum/shirdel-coder-9b-claude-fable-5",
	filename="Shirdel-Coder-9B-Claude-Fable-5.Q6_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Tesleum/shirdel-coder-9b-claude-fable-5 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
# Run inference directly in the terminal:
llama cli -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
# Run inference directly in the terminal:
llama cli -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

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 Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

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 Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

Use Docker

docker model run hf.co/Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

LM Studio
Jan
Ollama
How to use Tesleum/shirdel-coder-9b-claude-fable-5 with Ollama:
```
ollama run hf.co/Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
```

Unsloth Studio

How to use Tesleum/shirdel-coder-9b-claude-fable-5 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 Tesleum/shirdel-coder-9b-claude-fable-5 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 Tesleum/shirdel-coder-9b-claude-fable-5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tesleum/shirdel-coder-9b-claude-fable-5 to start chatting

How to use Tesleum/shirdel-coder-9b-claude-fable-5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

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": "Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tesleum/shirdel-coder-9b-claude-fable-5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

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 Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Tesleum/shirdel-coder-9b-claude-fable-5 with Docker Model Runner:
```
docker model run hf.co/Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K
```

Lemonade

How to use Tesleum/shirdel-coder-9b-claude-fable-5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tesleum/shirdel-coder-9b-claude-fable-5:Q6_K

Run and chat with the model

lemonade run user.shirdel-coder-9b-claude-fable-5-Q6_K

List all available models

lemonade list

shirdel-coder-9b-claude-fable-5 : GGUF

Claude Fable 5–style instruction tuning combined with Evol-Instruct-Code-80k-v1. Fine-tuned and converted to GGUF format using Q6_K quantization.

Example usage:

For text only LLMs: llama-cli -hf Tesleum/shirdel-coder-9b-claude-fable-5 --jinja
For multimodal models: llama-mtmd-cli -hf Tesleum/shirdel-coder-9b-claude-fable-5 --jinja

Available Model files:

Shirdel-Coder-9B-Claude-Fable-5.Q6_K.gguf
Shirdel-Coder-9B-Claude-Fable-5.BF16-mmproj.gguf

Downloads last month: 2,935

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

6-bit

Video Preview

Reinforcement Learning

Model tree for Tesleum/shirdel-coder-9b-claude-fable-5

Base model

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

Quantized

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

Quantized

(2)

this model

Tesleum
/

shirdel-coder-9b-claude-fable-5

shirdel-coder-9b-claude-fable-5 : GGUF

Available Model files:

Model tree for Tesleum/shirdel-coder-9b-claude-fable-5

Datasets used to train Tesleum/shirdel-coder-9b-claude-fable-5