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"

bharathmuppa commited on 3 days ago

Commit

8a0f78a

verified ·

1 Parent(s): de5839f

Upload fused model

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README.md +6 -4
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -75,17 +75,19 @@ The model was trained with this system prompt. Use it for best results:
 | Setting | Value |
 | --- | --- |
-| Records (train / valid / test) | 1869 / 101 / 60 |
-| Iterations | 1400 |
 | Batch size | 4 |
 | Max sequence length | 2048 |
 | Loss | assistant tokens only |
-| Final validation loss | 1.386 (from 2.147) |
-| Test loss, assistant tokens (base → tuned) | 2.148 → 1.346 |
 | Learning rate | 3e-5, cosine decay, 50 warm-up steps |
 ## Limits
 - 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.

 | 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.

model.safetensors CHANGED Viewed

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