Instructions to use LLJYY/SEALION-TC-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLJYY/SEALION-TC-v1 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LLJYY/SEALION-TC-v1", dtype="auto")

llama-cpp-python

How to use LLJYY/SEALION-TC-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LLJYY/SEALION-TC-v1",
	filename="sealion-checkpoint-1000-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 LLJYY/SEALION-TC-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LLJYY/SEALION-TC-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LLJYY/SEALION-TC-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LLJYY/SEALION-TC-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LLJYY/SEALION-TC-v1: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 LLJYY/SEALION-TC-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LLJYY/SEALION-TC-v1: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 LLJYY/SEALION-TC-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LLJYY/SEALION-TC-v1:Q4_K_M

Use Docker

docker model run hf.co/LLJYY/SEALION-TC-v1:Q4_K_M

LM Studio
Jan
Ollama
How to use LLJYY/SEALION-TC-v1 with Ollama:
```
ollama run hf.co/LLJYY/SEALION-TC-v1:Q4_K_M
```

Unsloth Studio new

How to use LLJYY/SEALION-TC-v1 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 LLJYY/SEALION-TC-v1 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 LLJYY/SEALION-TC-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LLJYY/SEALION-TC-v1 to start chatting

Pi new

How to use LLJYY/SEALION-TC-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LLJYY/SEALION-TC-v1: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": "LLJYY/SEALION-TC-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LLJYY/SEALION-TC-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LLJYY/SEALION-TC-v1: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 LLJYY/SEALION-TC-v1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use LLJYY/SEALION-TC-v1 with Docker Model Runner:
```
docker model run hf.co/LLJYY/SEALION-TC-v1:Q4_K_M
```

Lemonade

How to use LLJYY/SEALION-TC-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LLJYY/SEALION-TC-v1:Q4_K_M

Run and chat with the model

lemonade run user.SEALION-TC-v1-Q4_K_M

List all available models

lemonade list

Model Card for SeaLION-TC v1 (Tool Calling)

SeaLION-TC v1 is a specialized QLoRA fine-tune of aisingapore/Qwen-SEA-LION-v4-8B-VL, engineered specifically for Agentic Workflow Orchestration and Function Calling.

Unlike general-purpose chat models, this adapter was trained to enforce strict syntax compliance for tool usage while prioritizing safety (hallucination resistance). It is designed to act as a reliable "Edge Agent" for orchestrating multi-step tasks in regional contexts.

This model was built for HackRift 2025 at Singapore Institute of Technology

🏆 Benchmark Performance (BFCL v4)

This model was evaluated on the Berkeley Function Calling Leaderboard (BFCL v4) against the base SeaLION Instruct model.

Key Result: We achieved a +12% improvement in Safety (Irrelevance) and a +25% improvement in Real-World Multitasking (Live Parallel) compared to the base model.

Metric	SeaLION Base	SeaLION-TC v1	Delta	Analysis
Irrelevance (Safety)	79.17%	91.25%	🟢 +12.08%	significantly reduced hallucinated tool calls during casual conversation.
Live Parallel	50.00%	75.00%	🟢 +25.00%	Massive gain in handling simultaneous, multi-intent requests.
Live Parallel Multiple	54.17%	70.83%	🟢 +16.66%	Improved orchestration of complex, concurrent tool calls.
Simple Python	95.00%	93.50%	🔴 -1.50%	Negligible trade-off for increased safety.
Simple JS	76.00%	70.00%	🔴 -6.00%	Known Limitation: Non-Python syntax degraded slightly.

The rest of the tests remain within margin of error or with slight improvements! Full benchmark suite and comparison to come

⚠️ Intended Use & Limitations

Best For:

Python-based Agentic Backends: The model is highly optimized for Python function definitions.
RAG Orchestration: Excellent at selecting relevant tools from long lists (Multiple score: 94.5%).
Edge Deployment: Optimized for 4-bit quantization (GGUF) on consumer hardware (e.g., NVIDIA GeForce, AMD Ryzen AI).

Known Limitations:

The "Alignment Tax": In exchange for higher safety and parallel reasoning, the model's ability to generate valid Javascript tool calls has regressed by ~5-6% compared to the base model.
Vision Capabilities: While based on a VLM, this fine-tune focused exclusively on text-based function calling. Vision-related tool usage has not been strictly benchmarked.

⚙️ Training procedure

This model was trained using TRL with QLoRA instruction tuning.

Training Hyperparameters

Compute: 1x NVIDIA RTX 3090 (24GB VRAM)
Precision: 4-bit (NF4) Quantization
LoRA Rank: 32
LoRA Alpha: 64
LoRA Dropout: 0.05
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Strategy: Checkpoint selection via Early Stopping based on Agentic Capability (BFCL v4) at Step 1000.

Citations (WIP)

Berkeley Function Calling Leaderboard:

@misc{patil2024gorilla,
    title={Gorilla: Large Language Model Connected with Massive APIs},
    author={Shishir Patil and Tianjun Zhang and Xin Wang and Joseph E. Gonzalez},
    year={2023},
    journal={arXiv preprint arXiv:2305.15334}
}

SeaLION (AI Singapore):

@article{sealion2024,
    title={SeaLION: Southeast Asian Languages In One Network},
    author={AI Singapore},
    year={2024}
}

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

4-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for LLJYY/SEALION-TC-v1

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

aisingapore/Qwen-SEA-LION-v4-8B-VL

Quantized

(3)

this model

Paper for LLJYY/SEALION-TC-v1

Gorilla: Large Language Model Connected with Massive APIs

Paper • 2305.15334 • Published May 24, 2023 • 7