Instructions to use bharathmuppa/spring-coder-30b-a3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bharathmuppa/spring-coder-30b-a3b with Transformers:

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

pipe = pipeline("text-generation", model="bharathmuppa/spring-coder-30b-a3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bharathmuppa/spring-coder-30b-a3b")
model = AutoModelForCausalLM.from_pretrained("bharathmuppa/spring-coder-30b-a3b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use bharathmuppa/spring-coder-30b-a3b with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bharathmuppa/spring-coder-30b-a3b 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 bharathmuppa/spring-coder-30b-a3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bharathmuppa/spring-coder-30b-a3b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bharathmuppa/spring-coder-30b-a3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bharathmuppa/spring-coder-30b-a3b: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 bharathmuppa/spring-coder-30b-a3b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bharathmuppa/spring-coder-30b-a3b: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 bharathmuppa/spring-coder-30b-a3b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bharathmuppa/spring-coder-30b-a3b:Q4_K_M

Use Docker

docker model run hf.co/bharathmuppa/spring-coder-30b-a3b:Q4_K_M

LM Studio
Jan

vLLM

How to use bharathmuppa/spring-coder-30b-a3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bharathmuppa/spring-coder-30b-a3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bharathmuppa/spring-coder-30b-a3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bharathmuppa/spring-coder-30b-a3b:Q4_K_M

SGLang

How to use bharathmuppa/spring-coder-30b-a3b 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 "bharathmuppa/spring-coder-30b-a3b" \
    --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": "bharathmuppa/spring-coder-30b-a3b",
		"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 "bharathmuppa/spring-coder-30b-a3b" \
        --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": "bharathmuppa/spring-coder-30b-a3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bharathmuppa/spring-coder-30b-a3b with Ollama:
```
ollama run hf.co/bharathmuppa/spring-coder-30b-a3b:Q4_K_M
```
Unsloth Desktop

How to use bharathmuppa/spring-coder-30b-a3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bharathmuppa/spring-coder-30b-a3b: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": "bharathmuppa/spring-coder-30b-a3b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bharathmuppa/spring-coder-30b-a3b with Docker Model Runner:
```
docker model run hf.co/bharathmuppa/spring-coder-30b-a3b:Q4_K_M
```

Lemonade

How to use bharathmuppa/spring-coder-30b-a3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bharathmuppa/spring-coder-30b-a3b:Q4_K_M

Run and chat with the model

lemonade run user.spring-coder-30b-a3b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bharathmuppa/spring-coder-30b-a3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bharathmuppa/spring-coder-30b-a3b: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 bharathmuppa/spring-coder-30b-a3b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bharathmuppa/spring-coder-30b-a3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bharathmuppa/spring-coder-30b-a3b: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 "bharathmuppa/spring-coder-30b-a3b: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"

spring-coder-30b-a3b

A code model that is an expert in Spring Boot. It knows Spring Boot 3 and 4, Spring Framework 6 and 7, Spring Security 6, Spring Data JPA, Hibernate, MongoDB, Redis and Spring Cloud. It also knows Keycloak, Okta and Open Policy Agent. It writes modern Java 21 and Spring code. It was trained to write complete, compiling projects that follow one coding guideline (docs/SPRING-GUIDELINES.md in the source repository).

Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct
Method: LoRA (rank 16, attention projections q/k/v/o), merged into the base weights in bf16
Trained with transformers and peft on one NVIDIA 80 GB GPU
Data: bharathmuppa/spring-instruct (4613 instruction pairs: 4012 authored records on Spring Boot, Spring Security, Spring Data, Spring Cloud, Hibernate, Keycloak, Okta, Open Policy Agent, version facts and known issues, with 360 compiled project records, and 601 CodeAlpaca Java records in train only, CC-BY-4.0)

The weights are the full merged model in bf16, so this repository is large. The adapter alone is in bharathmuppa/spring-coder-30b-a3b-lora.

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
m = "bharathmuppa/spring-coder-30b-a3b"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, dtype="auto", device_map="auto")
msgs = [{"role": "system", "content": SYSTEM},
        {"role": "user", "content": "Write a SecurityFilterChain bean for Spring Security 6 that protects /api/** with JWT and leaves /actuator/health open."}]
text = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)
enc = tok(text, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(**enc, max_new_tokens=800)[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))

The model was trained with this system prompt (SYSTEM above). Use it for best results:

You are an expert Spring Boot engineer. You know Spring Boot 3 and 4, Spring Framework 6 and 7, Spring Security 6, Spring Data, Spring Cloud, Hibernate, Keycloak, Okta and Open Policy Agent. Answer with correct, modern, idiomatic Java 21 and Spring code and say which Spring Boot version a feature needs when it matters.

Use with Ollama / llama.cpp (GGUF)

This repo also holds GGUF builds, made on the training pod with llama.cpp: spring-coder-30b-a3b-Q4_K_M.gguf (about 18 GB, needs about 20 GB of memory) and spring-coder-30b-a3b-Q8_0.gguf (about 32 GB), plus a Modelfile with the system prompt and chat template.

ollama run hf.co/bharathmuppa/spring-coder-30b-a3b:Q4_K_M

Or build it locally from the Modelfile:

hf download bharathmuppa/spring-coder-30b-a3b --include "spring-coder-30b-a3b-Q4_K_M.gguf" "Modelfile" --local-dir spring-coder
cd spring-coder && ollama create spring-coder -f Modelfile && ollama run spring-coder

Q8_0 is closest to the bf16 weights; Q4_K_M is fine for scaffolding. On Apple Silicon mlx-lm can also load the bf16 weights directly:

pip install mlx-lm
python -m mlx_lm convert --hf-path bharathmuppa/spring-coder-30b-a3b -q --q-bits 4 --mlx-path spring-coder-4bit
python -m mlx_lm generate --model spring-coder-4bit --prompt "Write a SecurityFilterChain bean that uses JWT."

Training

Setting	Value
Records (train / valid / test)	4293 / 200 / 120
Steps done	1074 optimizer steps (2.0 epochs; the loop has a wall-clock limit of 180 minutes)
Final loss	train 1.056, eval 1.1449
Batch	1 x 8 gradient accumulation, max 4096 tokens
Learning rate	3e-5, cosine, 50 warm-up steps
LoRA	rank 16, alpha 16, dropout 0.05, q/k/v/o
Loss	answer tokens only

The run log with every loss line is in the dataset repository bharathmuppa/angular-lm-runs. The dataset builder and the checker that compiles the project records are in the spring-lm repository. The training pipeline is in the GitLab project mahaamatsya/ai/angular-lm.

Limits

The model writes scaffolds well. Logic can still have defects, so review the output and run the compiler and the tests. Version facts after early 2026 come only from the training data.

Releases

Each Spring Boot version is a git tag on the Hub. The tag of this upload is below. It marks the files of that version. Choose the tag in the branch menu, or add revision="<tag>" when you download. The main branch holds the latest release.

This upload has no release tag.

Downloads last month: 2

Safetensors

Model size

31B params

Tensor type

BF16

Model tree for bharathmuppa/spring-coder-30b-a3b

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Adapter

(62)

this model

bharathmuppa
/

spring-coder-30b-a3b

spring-coder-30b-a3b

Use with transformers

Use with Ollama / llama.cpp (GGUF)

Training

Limits

Releases

Model tree for bharathmuppa/spring-coder-30b-a3b

Dataset used to train bharathmuppa/spring-coder-30b-a3b