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

llama.cpp

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

Use Docker

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

LM Studio
Jan

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:Q4_K_M

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?"
			}
		]
	}'

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

How to use bharathmuppa/angular-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/angular-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/angular-coder-30b-a3b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use bharathmuppa/angular-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/angular-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/angular-coder-30b-a3b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bharathmuppa/angular-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/angular-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/angular-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"

angular-coder-30b-a3b / README.md

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Upload fused model

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---
license: apache-2.0
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
language:
  - en
tags:
  - angular
  - gguf
  - ollama
  - typescript
  - frontend
  - code
  - lora
  - peft
datasets:
  - bharathmuppa/angular-instruct
pipeline_tag: text-generation
library_name: transformers
---
# 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

```python
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.

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

Or build it locally from the Modelfile:

```sh
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:
```sh
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.

	---
	license: apache-2.0
	base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
	language:
	- en
	tags:
	- angular
	- gguf
	- ollama
	- typescript
	- frontend
	- code
	- lora
	- peft
	datasets:
	- bharathmuppa/angular-instruct
	pipeline_tag: text-generation
	library_name: transformers
	---

	# 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

	```python
	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.

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

	Or build it locally from the Modelfile:

	```sh
	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:

	```sh
	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.