Instructions to use prithivMLmods/Aztec-Coder-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Aztec-Coder-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Aztec-Coder-4B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Aztec-Coder-4B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Aztec-Coder-4B-GGUF 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 prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Aztec-Coder-4B-GGUF: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 prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Aztec-Coder-4B-GGUF: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 prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Aztec-Coder-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Aztec-Coder-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Aztec-Coder-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Aztec-Coder-4B-GGUF 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 "prithivMLmods/Aztec-Coder-4B-GGUF" \
    --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": "prithivMLmods/Aztec-Coder-4B-GGUF",
		"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 "prithivMLmods/Aztec-Coder-4B-GGUF" \
        --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": "prithivMLmods/Aztec-Coder-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Aztec-Coder-4B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/Aztec-Coder-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Aztec-Coder-4B-GGUF: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": "prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/Aztec-Coder-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Aztec-Coder-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aztec-Coder-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/Aztec-Coder-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Aztec-Coder-4B-GGUF: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 prithivMLmods/Aztec-Coder-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/Aztec-Coder-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Aztec-Coder-4B-GGUF: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 "prithivMLmods/Aztec-Coder-4B-GGUF: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"

prithivMLmods commited on 24 days ago

Commit

685f71a

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1 Parent(s): 9cc9ee4

Update README.md

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@@ -1,4 +1,11 @@
 ---
 base_model:
 - jsbaicenter/JSBAI-Coder-4B
----

 ---
 base_model:
 - jsbaicenter/JSBAI-Coder-4B
+library_name: transformers
+tags:
+- text-generation-inference
+---
+# **JSBAI-Coder-4B-GGUF**
+> **JSBAI-Coder-4B** is a 4-billion-parameter agentic coding model from San Diego State University's James Silberrad Brown Center for AI Research (JSBCAI), fine-tuned from Qwen3.5-4B to investigate bugs, edit files, run commands, and verify its own fixes in real software repositories — a capability class the authors note has typically required 27B+ models — while fitting on consumer hardware (~8GB VRAM in BF16, ~5GB as an NVFP4 quantized variant). It was trained in three stages: seed demonstrations from ~1,875 test-verified coding trajectories generated by GLM-5.3, reinforcement learning via 145 batches of on-policy GRPO over 237 curated software-engineering problems using test-pass/fail as the only reward signal, and repeated generalization checks against unseen bugs, alongside general instruction-following data drawn from NVIDIA's Nemotron-Post-Training-Dataset-v2. On 121 held-out, never-trained-on real bugs verified by running each project's hidden test suite, it jumped from 10.1% (base Qwen3.5-4B) to 82.9% solve rate, while also improving general capabilities rather than trading them off — IFEval rose from 84.66 to 87.21 and MMLU-Pro from 64.0% to 70.0% — alongside gains on Live-60 real-world engineering tasks (15.0% to 21.7%) and 33.8% on Terminal-Bench 2.1. It's served via vLLM with the Qwen3.5 chat template, `qwen3` reasoning parser, and `qwen3_coder` tool-call format, is tuned specifically for sandboxed container agent loops rather than general deployment, inherits its safety behavior unmodified from the base model (the RL phase optimized purely for test-passing with no safety-specific training), and is released under Apache-2.0.