Instructions to use bharathmuppa/angular-coder-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bharathmuppa/angular-coder-1.5b with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("bharathmuppa/angular-coder-1.5b")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bharathmuppa/angular-coder-1.5b 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-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bharathmuppa/angular-coder-1.5b: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-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bharathmuppa/angular-coder-1.5b: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-1.5b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bharathmuppa/angular-coder-1.5b: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-1.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bharathmuppa/angular-coder-1.5b:Q4_K_M

Use Docker

docker model run hf.co/bharathmuppa/angular-coder-1.5b:Q4_K_M

LM Studio
Jan

vLLM

How to use bharathmuppa/angular-coder-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bharathmuppa/angular-coder-1.5b"
# 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-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bharathmuppa/angular-coder-1.5b:Q4_K_M

Ollama
How to use bharathmuppa/angular-coder-1.5b with Ollama:
```
ollama run hf.co/bharathmuppa/angular-coder-1.5b:Q4_K_M
```
Unsloth Desktop

How to use bharathmuppa/angular-coder-1.5b with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bharathmuppa/angular-coder-1.5b"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bharathmuppa/angular-coder-1.5b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use bharathmuppa/angular-coder-1.5b with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "bharathmuppa/angular-coder-1.5b"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "bharathmuppa/angular-coder-1.5b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "bharathmuppa/angular-coder-1.5b",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use bharathmuppa/angular-coder-1.5b with Docker Model Runner:
```
docker model run hf.co/bharathmuppa/angular-coder-1.5b:Q4_K_M
```

Lemonade

How to use bharathmuppa/angular-coder-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bharathmuppa/angular-coder-1.5b:Q4_K_M

Run and chat with the model

lemonade run user.angular-coder-1.5b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bharathmuppa/angular-coder-1.5b with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bharathmuppa/angular-coder-1.5b"

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-1.5b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bharathmuppa/angular-coder-1.5b with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bharathmuppa/angular-coder-1.5b"

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-1.5b" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

angular-coder-1.5b / README.md

bharathmuppa

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
language:
  - en
tags:
  - angular
  - gguf
  - ollama
  - typescript
  - frontend
  - code
  - mlx
  - lora
datasets:
  - bharathmuppa/angular-instruct
pipeline_tag: text-generation
library_name: mlx
---
# angular-coder-1.5b

A small 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 new application builder and the `ng update` migrations.

- Base model: `Qwen/Qwen2.5-Coder-1.5B-Instruct`
- Method: LoRA (rank 16, last 16 layers, attention + MLP), fused into the base weights
- Trained with `mlx-lm` on an Apple M2 Max
- Data: `bharathmuppa/angular-instruct` (2030 instruction pairs written from the official Angular docs and changelog)

## Use with mlx-lm (Apple Silicon)

```sh
pip install mlx-lm
python -m mlx_lm generate --model bharathmuppa/angular-coder-1.5b \
  --prompt "Which Angular version introduced @defer and how do I use it?"
```

## Use with Ollama / llama.cpp (GGUF)

This repo also holds GGUF builds: `angular-coder-1.5b-Q4_K_M.gguf` (1.0 GB), `angular-coder-1.5b-Q8_0.gguf` (1.6 GB) and `angular-coder-1.5b-f16.gguf` (3.1 GB), plus a `Modelfile` with the system prompt and chat template.

```sh
ollama run hf.co/bharathmuppa/angular-coder-1.5b:Q8_0
```

Or build it locally from the Modelfile:

```sh
hf download bharathmuppa/angular-coder-1.5b --include "angular-coder-1.5b-Q8_0.gguf" "Modelfile" --local-dir angular-coder
cd angular-coder && sed -i '' 's/Q4_K_M/Q8_0/' Modelfile && ollama create angular-coder -f Modelfile && ollama run angular-coder
```

Q8_0 is recommended for version facts; Q4_K_M is fine for scaffolding. Both were checked against the full model on held-out questions.
## Use with transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
m = "bharathmuppa/angular-coder-1.5b"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="auto", device_map="auto")
msgs = [{"role": "user", "content": "Write a standalone Angular 22 component with a required signal input."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=500)[0][ids.shape[1]:], skip_special_tokens=True))
```
The model was trained with this system prompt. 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.
## Training
| Setting | Value |
| --- | --- |
| Records (train / valid / test) | 2427 / 131 / 79 |
| Iterations | 1700 |
| Batch size | 4 |
| Max sequence length | 2048 |
| Loss | assistant tokens only |
| Final validation loss | 1.313 (from 2.175) |
| Test loss, assistant tokens (base → tuned) | 2.104 → 1.293 |
| Learning rate | 3e-5, cosine decay, 50 warm-up steps |
## Limits
- Known-issue answers ("I'm on 19.2 and see X") give the right shape, release line and fix description, but the issue and PR numbers are often wrong. Verify them on GitHub. A 1.5B model does not memorize 250 issue numbers from a few exposures.
- Use `repetition_penalty` 1.1 or similar; long import lists can repeat otherwise.
- 1.5B parameters. It is good for Angular questions and small components. It is not a general assistant.
- Knowledge ends at Angular 22 (October 2026).
- Check generated code before you ship it.
## Sources
Training records were written from the Angular documentation
(`angular/angular`, `adev/src/content`, MIT license) and the Angular changelog.

	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	language:
	- en
	tags:
	- angular
	- gguf
	- ollama
	- typescript
	- frontend
	- code
	- mlx
	- lora
	datasets:
	- bharathmuppa/angular-instruct
	pipeline_tag: text-generation
	library_name: mlx
	---

	# angular-coder-1.5b

	A small 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 new application builder and the `ng update` migrations.

	- Base model: `Qwen/Qwen2.5-Coder-1.5B-Instruct`
	- Method: LoRA (rank 16, last 16 layers, attention + MLP), fused into the base weights
	- Trained with `mlx-lm` on an Apple M2 Max
	- Data: `bharathmuppa/angular-instruct` (2030 instruction pairs written from the official Angular docs and changelog)

	## Use with mlx-lm (Apple Silicon)

	```sh
	pip install mlx-lm
	python -m mlx_lm generate --model bharathmuppa/angular-coder-1.5b \
	--prompt "Which Angular version introduced @defer and how do I use it?"
	```

	## Use with Ollama / llama.cpp (GGUF)

	This repo also holds GGUF builds: `angular-coder-1.5b-Q4_K_M.gguf` (1.0 GB), `angular-coder-1.5b-Q8_0.gguf` (1.6 GB) and `angular-coder-1.5b-f16.gguf` (3.1 GB), plus a `Modelfile` with the system prompt and chat template.

	```sh
	ollama run hf.co/bharathmuppa/angular-coder-1.5b:Q8_0
	```

	Or build it locally from the Modelfile:

	```sh
	hf download bharathmuppa/angular-coder-1.5b --include "angular-coder-1.5b-Q8_0.gguf" "Modelfile" --local-dir angular-coder
	cd angular-coder && sed -i '' 's/Q4_K_M/Q8_0/' Modelfile && ollama create angular-coder -f Modelfile && ollama run angular-coder
	```

	Q8_0 is recommended for version facts; Q4_K_M is fine for scaffolding. Both were checked against the full model on held-out questions.

	## Use with transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	m = "bharathmuppa/angular-coder-1.5b"
	tok = AutoTokenizer.from_pretrained(m)
	model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="auto", device_map="auto")
	msgs = [{"role": "user", "content": "Write a standalone Angular 22 component with a required signal input."}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
	print(tok.decode(model.generate(ids, max_new_tokens=500)[0][ids.shape[1]:], skip_special_tokens=True))
	```

	The model was trained with this system prompt. 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.

	## Training

	\| Setting \| Value \|
	\| --- \| --- \|
	\| Records (train / valid / test) \| 2427 / 131 / 79 \|
	\| Iterations \| 1700 \|
	\| Batch size \| 4 \|
	\| Max sequence length \| 2048 \|
	\| Loss \| assistant tokens only \|
	\| Final validation loss \| 1.313 (from 2.175) \|
	\| Test loss, assistant tokens (base → tuned) \| 2.104 → 1.293 \|
	\| Learning rate \| 3e-5, cosine decay, 50 warm-up steps \|

	## Limits

	- Known-issue answers ("I'm on 19.2 and see X") give the right shape, release line and fix description, but the issue and PR numbers are often wrong. Verify them on GitHub. A 1.5B model does not memorize 250 issue numbers from a few exposures.

	- Use `repetition_penalty` 1.1 or similar; long import lists can repeat otherwise.

	- 1.5B parameters. It is good for Angular questions and small components. It is not a general assistant.
	- Knowledge ends at Angular 22 (October 2026).
	- Check generated code before you ship it.

	## Sources

	Training records were written from the Angular documentation
	(`angular/angular`, `adev/src/content`, MIT license) and the Angular changelog.