Instructions to use Jay2003Bhatt/alpha-coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Jay2003Bhatt/alpha-coder-14b 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 Jay2003Bhatt/alpha-coder-14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Jay2003Bhatt/alpha-coder-14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Jay2003Bhatt/alpha-coder-14b: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 Jay2003Bhatt/alpha-coder-14b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jay2003Bhatt/alpha-coder-14b: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 Jay2003Bhatt/alpha-coder-14b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Use Docker

docker model run hf.co/Jay2003Bhatt/alpha-coder-14b:Q4_K_M

LM Studio
Jan

vLLM

How to use Jay2003Bhatt/alpha-coder-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jay2003Bhatt/alpha-coder-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jay2003Bhatt/alpha-coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Ollama
How to use Jay2003Bhatt/alpha-coder-14b with Ollama:
```
ollama run hf.co/Jay2003Bhatt/alpha-coder-14b:Q4_K_M
```

Unsloth Studio

How to use Jay2003Bhatt/alpha-coder-14b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jay2003Bhatt/alpha-coder-14b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jay2003Bhatt/alpha-coder-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jay2003Bhatt/alpha-coder-14b to start chatting

How to use Jay2003Bhatt/alpha-coder-14b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Jay2003Bhatt/alpha-coder-14b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Jay2003Bhatt/alpha-coder-14b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jay2003Bhatt/alpha-coder-14b: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 "Jay2003Bhatt/alpha-coder-14b: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"

Docker Model Runner
How to use Jay2003Bhatt/alpha-coder-14b with Docker Model Runner:
```
docker model run hf.co/Jay2003Bhatt/alpha-coder-14b:Q4_K_M
```

Lemonade

How to use Jay2003Bhatt/alpha-coder-14b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Run and chat with the model

lemonade run user.alpha-coder-14b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Jay2003Bhatt/alpha-coder-14b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jay2003Bhatt/alpha-coder-14b: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 Jay2003Bhatt/alpha-coder-14b:Q4_K_M

Run Hermes

hermes

Atomic Chat

alpha-coder-14b / README.md

Jay2003Bhatt

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	---
	base_model: Qwen/Qwen2.5-Coder-14B-Instruct
	license: apache-2.0
	language:
	- en
	tags:
	- code
	- coding-assistant
	- lora
	- fine-tuned
	- gguf
	- ollama
	pipeline_tag: text-generation
	---

	# Alpha-Coder-14B

	Alpha-Coder-14B is a fine-tuned version of [Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct), adapted via LoRA to produce typed, tested Python code. This repo contains both the fused fp16 weights and a Q4_K_M GGUF quant for local inference (e.g. with Ollama or llama.cpp).

	## Base model attribution

	This model is a derivative of Qwen/Qwen2.5-Coder-14B-Instruct, released by the Qwen team under the Apache 2.0 license. Alpha-Coder-14B is redistributed under the same license, as permitted by Apache 2.0 for derivative/renamed works, with attribution to the original model and authors.

	- Base model: [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
	- License: Apache 2.0

	## Training details

	- Method: LoRA fine-tuning
	- Hardware: Apple Silicon M5, 24GB unified memory
	- Framework: MLX (4-bit base model during training)
	- LoRA config: rank = 64, alpha = 128, learning rate = 2e-6
	- Steps: 6,160
	- Validation loss: 0.383 → 0.252
	- Post-training: LoRA adapter fused into the base model, dequantized to fp16 HF safetensors, then converted and quantized to GGUF (Q4_K_M, 8.4GB) via llama.cpp

	## Benchmarks

	\| Benchmark \| Base (Qwen2.5-Coder-14B-Instruct) \| Alpha-Coder-14B \|
	\|---\|---\|---\|
	\| MMLU \| 72% \| 77% \|
	\| GSM8K \| ~93% (no regression) \| 93% \|

	No measurable forgetting was observed on GSM8K after fine-tuning, while MMLU improved by 5 points.

	## Files in this repo

	\| File \| Description \|
	\|---\|---\|
	\| `*.safetensors` \| Fused fp16 weights (LoRA merged into base), full precision \|
	\| `tokenizer` / `.json` \| Tokenizer and config files \|
	\| `alpha-14b-Q4_K_M.gguf` \| Q4_K_M quantized GGUF, ~8.4GB, for llama.cpp / Ollama \|

	## Usage with Ollama

	1. Download `alpha-14b-Q4_K_M.gguf` from this repo.
	2. Create a `Modelfile` in the same directory (use your actual system prompt from your local Modelfile).
	3. Build and run:

	```bash
	ollama create alpha-coder -f Modelfile
	ollama run alpha-coder
	```

	## Usage with llama.cpp

	```bash
	./llama-cli -m alpha-14b-Q4_K_M.gguf -p "Write a Python function that ..."
	```

	## Usage with transformers (fp16 safetensors)

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("Jay2003Bhatt/alpha-coder-14b", torch_dtype="auto", device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained("Jay2003Bhatt/alpha-coder-14b")
	```

	## Intended use

	Alpha-Coder-14B is intended as a coding assistant producing typed, tested Python code. As with any fine-tuned model, evaluate outputs before relying on them in production, particularly for correctness and security-sensitive code.

	## License

	Apache 2.0, inherited from the base model. See the [Qwen2.5-Coder-14B-Instruct license](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct/blob/main/LICENSE) for details.