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

Libraries

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

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

pipe = pipeline("text-generation", model="bharathmuppa/angular-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/angular-coder-30b-a3b")
model = AutoModelForCausalLM.from_pretrained("bharathmuppa/angular-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/angular-coder-30b-a3b with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/bharathmuppa/angular-coder-30b-a3b

SGLang

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

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

angular-coder-30b-a3b

A code model that is an expert in Angular. It knows each Angular version from v14 to v22. It writes modern Angular: standalone components, signals, inject(), @if / @for, @defer, zoneless change detection, Signal Forms, the @Service decorator, the application builder and the ng update migrations. It was trained to write complete, compiling projects that follow one coding guideline (docs/ANGULAR-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 A100 80GB
Data: bharathmuppa/angular-instruct (3461 instruction pairs: Angular docs, version facts, known issues, Material, CDK, NgRx, angular-eslint, the CLI, and 400 compiled and lint-checked project records)

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

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
m = "bharathmuppa/angular-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 standalone Angular 22 component with a required signal input."}]
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 Angular engineer. You know every Angular version from v12 to the current release: NgModules, standalone components, signals, the new control flow, inject(), deferrable views, zoneless change detection, SSR, the CLI and migrations. Answer with correct, modern, idiomatic TypeScript and say which Angular 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: angular-coder-30b-a3b-Q4_K_M.gguf (about 18 GB, needs about 20 GB of memory) and angular-coder-30b-a3b-Q8_0.gguf (about 32 GB), plus a Modelfile with the system prompt and chat template.

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

Or build it locally from the Modelfile:

hf download bharathmuppa/angular-coder-30b-a3b --include "angular-coder-30b-a3b-Q4_K_M.gguf" "Modelfile" --local-dir angular-coder
cd angular-coder && ollama create angular-coder -f Modelfile && ollama run angular-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/angular-coder-30b-a3b -q --q-bits 4 --mlx-path angular-coder-4bit
python -m mlx_lm generate --model angular-coder-4bit --prompt "What is new in Angular 22?"

Training

Setting	Value
Records (train / valid / test)	3182 / 172 / 103
Steps done	796 optimizer steps (2.0 epochs; the loop has a wall-clock limit of 170 minutes)
Final loss	train 1.1279, eval 1.1662
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 source, the dataset builder, the checker that compiles and lints the project records, and the evaluation script are 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 linter. Version facts after early 2026 come only from the training data.

Downloads last month: -

Safetensors

Model size

31B params

Tensor type

BF16

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

Base model

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

Adapter

(62)

this model

bharathmuppa
/

angular-coder-30b-a3b