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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Devwa/jackSparrow 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 Devwa/jackSparrow:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Devwa/jackSparrow:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Devwa/jackSparrow:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Devwa/jackSparrow:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Devwa/jackSparrow:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Devwa/jackSparrow:Q4_K_M

Use Docker

docker model run hf.co/Devwa/jackSparrow:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use Devwa/jackSparrow with Ollama:
```
ollama run hf.co/Devwa/jackSparrow:Q4_K_M
```
Unsloth Desktop

How to use Devwa/jackSparrow with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Devwa/jackSparrow:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Devwa/jackSparrow:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use Devwa/jackSparrow with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Devwa/jackSparrow:Q4_K_M

Run and chat with the model

lemonade run user.jackSparrow-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Devwa/jackSparrow with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Devwa/jackSparrow: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 Devwa/jackSparrow:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Devwa/jackSparrow with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Devwa/jackSparrow:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Devwa/jackSparrow:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Model Card for Model ID

This model is a fine tuned Llama 3.2 to behave like Jack Sparrow.

Model Details

Model Description

This LLaMA model was fine-tuned on a custom dataset of Jack Sparrow's dialogue extracted from movie scripts, quotes, and paraphrased pirate-style interactions. It's designed to be used in conversational chat interfaces or roleplay environments.

Developed by: ebartha
Model type: Chat bot
Language(s) (NLP): English
Finetuned from model [optional]: Llama-3.2-3B-Instruct

Uses

Direct Use

This model can be used as:

A conversational AI chatbot for fans of Pirates of the Caribbean
A pirate-themed character in a storytelling game or NPC
A personality layer on top of LLM agents (e.g., LangChain, CrewAI)

Out-of-Scope Use

Not suitable for factual Q&A or critical decision-making
Should not be used to impersonate real people or used in harmful, deceptive, or offensive contexts

Bias, Risks, and Limitations

While the model is designed to be humorous and fictional, it is trained on stylized and potentially stereotypical data from movies. Users should not take responses literally or assume cultural accuracy.

Recommendations

Use in entertainment, educational, or fictional settings. Always clarify that the chatbot is a fictional character, not an actual pirate or historical figure.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

The model was fine-tuned on a custom dataset of Jack Sparrow dialogue: Devwa/jackSparrow. The dataset contains over 600 carefully curated and cleaned lines of dialogue from the Pirates of the Caribbean movies, formatted in a ShareGPT-like structure. Each line is embedded in conversational context with pirate-themed prompts, randomized to improve generalization and make the model more interactive.

The data was preprocessed to remove stage directions, formal/non-character lines, and noise (e.g. web references or citations), with additional injections of characteristic Jack Sparrow phrases.

Training Procedure

The model was fine-tuned using Unsloth's SFTTrainer, which wraps around Hugging Face's transformers and trl. The base model was meta-llama/Llama-3.2-3B-Instruct.

Training was conducted using full parameter fine-tuning with LoRA adapters. Each training sample consisted of a single formatted conversation with a user prompt and Jack Sparrow-style response.

Preprocessing [optional]

The dataset text field was text, with pre-formatted prompt/response structure.

Packed sequences were disabled (packing=False) to retain natural dialogue flow.

Each prompt was padded/truncated to a maximum sequence length of 2048 tokens.

Training Hyperparameters

Training regime: mixed precision (fp16 if bfloat16 unsupported, bf16 otherwise)
Epochs: 1
Batch size: 2 (per device)
Gradient accumulation: 4 steps (effective batch size: 8)
Learning rate: 2e-4
Optimizer: AdamW (8-bit)
Weight decay: 0.01
LR scheduler: Linear
Warmup steps: 5
Seed: 3407
Output dir: outputs/

Speeds, Sizes, Times [optional]

PU = Tesla T4. Max memory = 14.748 GB. 2.635 GB of memory reserved. 1:36:40

Downloads last month: 16

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

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Model tree for Devwa/jackSparrow

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

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this model

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jackSparrow

Model Card for Model ID

Model Details

Model Description

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Speeds, Sizes, Times [optional]

Model tree for Devwa/jackSparrow

Dataset used to train Devwa/jackSparrow