Instructions to use RikZD/znx-coder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RikZD/znx-coder-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RikZD/znx-coder-v1", filename="znx-coder-v1-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RikZD/znx-coder-v1 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 RikZD/znx-coder-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf RikZD/znx-coder-v1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RikZD/znx-coder-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf RikZD/znx-coder-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 RikZD/znx-coder-v1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RikZD/znx-coder-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 RikZD/znx-coder-v1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RikZD/znx-coder-v1:Q4_K_M
Use Docker
docker model run hf.co/RikZD/znx-coder-v1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RikZD/znx-coder-v1 with Ollama:
ollama run hf.co/RikZD/znx-coder-v1:Q4_K_M
- Unsloth Studio
How to use RikZD/znx-coder-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 RikZD/znx-coder-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 RikZD/znx-coder-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RikZD/znx-coder-v1 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RikZD/znx-coder-v1 with Docker Model Runner:
docker model run hf.co/RikZD/znx-coder-v1:Q4_K_M
- Lemonade
How to use RikZD/znx-coder-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RikZD/znx-coder-v1:Q4_K_M
Run and chat with the model
lemonade run user.znx-coder-v1-Q4_K_M
List all available models
lemonade list
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RikZD/znx-coder-v1:Q4_K_M# Run inference directly in the terminal:
llama cli -hf RikZD/znx-coder-v1:Q4_K_MUse 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 RikZD/znx-coder-v1:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf RikZD/znx-coder-v1:Q4_K_MBuild 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 RikZD/znx-coder-v1:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf RikZD/znx-coder-v1:Q4_K_MUse Docker
docker model run hf.co/RikZD/znx-coder-v1:Q4_K_MQuick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-3B tags: - qwen2.5 - qwen2.5-coder - coding - fine-tuned - qlora - gguf language: - id - en
âš¡ ZNX-Coder-v1
ZNX-Coder-v1 is a fine-tuned coding AI built on top of Qwen2.5-Coder-3B, developed by ZNX-ai (Zenith System).
Specialized for code generation, debugging, and programming assistance in both English and Indonesian.
Model Details
| Base Model | Qwen/Qwen2.5-Coder-3B |
| Fine-tune Method | QLoRA (r=32, alpha=64) |
| Epochs | 3 |
| Learning Rate | 2e-5 |
| Hardware | NVIDIA T4 |
| Developer | ZNX-ai |
| Website | ZenithSystem.netlify.app |
Files
| File | Description |
|---|---|
znx-coder-v1-Q4_K_M.gguf |
4-bit quantized GGUF — ready to run with llama.cpp |
adapter/ |
LoRA adapter weights |
Quickstart
llama.cpp
./llama-cli -m znx-coder-v1-Q4_K_M.gguf \
--chat-template chatml \
-cnv
Python
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id="RikZD/znx-coder-v1",
filename="znx-coder-v1-Q4_K_M.gguf"
)
llm = Llama(model_path=model_path, n_ctx=2048)
response = llm.create_chat_completion(
messages=[
{
"role": "system",
"content": "You are ZNX-Coder-v1, a coding AI developed by Zenith System."
},
{
"role": "user",
"content": "Write a fibonacci function in Python"
}
],
temperature=0.1,
max_tokens=512,
)
print(response["choices"][0]["message"]["content"])
System Prompt
You are ZNX-Coder-v1, a coding AI based on Qwen2.5-Coder-3B,
fine-tuned by Zenith System.
Limitations
- Small training dataset (121 conversations) — occasional hallucinations may occur
- Optimized primarily for Indonesian language interactions
- Focused on coding tasks, not general-purpose assistant
License
Apache 2.0 ```
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Hardware compatibility
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4-bit
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf RikZD/znx-coder-v1:Q4_K_M# Run inference directly in the terminal: llama cli -hf RikZD/znx-coder-v1:Q4_K_M