Instructions to use mrsandip/Alfa-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrsandip/Alfa-Code with Transformers:

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

pipe = pipeline("text-generation", model="mrsandip/Alfa-Code")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mrsandip/Alfa-Code", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mrsandip/Alfa-Code 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 mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
llama cli -hf mrsandip/Alfa-Code:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
llama cli -hf mrsandip/Alfa-Code:Q4_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf mrsandip/Alfa-Code:Q4_0

Use Docker

docker model run hf.co/mrsandip/Alfa-Code:Q4_0

Llama new
LM Studio
Jan

vLLM

How to use mrsandip/Alfa-Code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mrsandip/Alfa-Code:Q4_0

SGLang

How to use mrsandip/Alfa-Code 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 "mrsandip/Alfa-Code" \
    --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": "mrsandip/Alfa-Code",
		"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 "mrsandip/Alfa-Code" \
        --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": "mrsandip/Alfa-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mrsandip/Alfa-Code with Ollama:
```
ollama run hf.co/mrsandip/Alfa-Code:Q4_0
```
Unsloth Desktop
Docker Model Runner
How to use mrsandip/Alfa-Code with Docker Model Runner:
```
docker model run hf.co/mrsandip/Alfa-Code:Q4_0
```

Lemonade

How to use mrsandip/Alfa-Code with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrsandip/Alfa-Code:Q4_0

Run and chat with the model

lemonade run user.Alfa-Code-Q4_0

List all available models

lemonade list

Atomic Chat

mrsandip commited on 26 days ago

Commit

774f0e5

verified ·

1 Parent(s): 2204915

Fix 401: add HF_TOKEN login cell

Browse files

Files changed (1) hide show

notebooks/colab_t4.ipynb +1 -47

notebooks/colab_t4.ipynb CHANGED Viewed

@@ -1,47 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": ["# Alfa-Code Tiny-50M — Colab Free T4\n", "Runtime → Change runtime type → T4 GPU. Then Run all."]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": ["!nvidia-smi --query-gpu=name,memory.total --format=csv\n", "!pip install -q torch transformers datasets tokenizers accelerate huggingface_hub"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": ["from huggingface_hub import snapshot_download\n", "snapshot_download('mrsandip/Alfa-Code', local_dir='/content/alfa', repo_type='model')\n", "%cd /content/alfa\n", "!ls scripts/ data/processed/"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": ["# bigger data (500 rows) for real train\n", "!python scripts/download_data.py --dataset code_small_test --max-rows 2000\n", "!python scripts/train_tokenizer.py\n", "!ls -lh tokenizers/ data/processed/"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": ["!python scripts/train_hf.py --data data/processed/train.jsonl --out /content/outputs/tiny-50M --epochs 1\n", "!ls -lh /content/outputs/tiny-50M"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": ["# push back to Hub (set HF_TOKEN in Colab Secrets)\n", "import os\n", "from huggingface_hub import HfApi\n", "tok = os.getenv('HF_TOKEN')\n", "api = HfApi(token=tok)\n", "api.upload_folder(folder_path='/content/outputs/tiny-50M', repo_id='mrsandip/Alfa-Code', path_in_repo='outputs/tiny-50M')\n", "print('uploaded')"]
-  }
- ],
- "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}},
- "nbformat": 4,
- "nbformat_minor": 5
-}


1	+ {"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Alfa-Code Tiny-50M \u00e2\u20ac\u201d Colab Free T4\n", "Runtime \u00e2\u2020\u2019 Change runtime type \u00e2\u2020\u2019 T4 GPU. Then Run all."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from huggingface_hub import login\n", "from google.colab import userdata\n", "tok = userdata.get('HF_TOKEN')\n", "login(token=tok)\n", "print('logged in')\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["!nvidia-smi --query-gpu=name,memory.total --format=csv\n", "!pip install -q torch transformers datasets tokenizers accelerate huggingface_hub"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from huggingface_hub import snapshot_download\n", "snapshot_download('mrsandip/Alfa-Code', local_dir='/content/alfa', repo_type='model', token=tok)\n", "%cd /content/alfa\n", "!ls scripts/ data/processed/"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# bigger data (500 rows) for real train\n", "!python scripts/download_data.py --dataset code_small_test --max-rows 2000\n", "!python scripts/train_tokenizer.py\n", "!ls -lh tokenizers/ data/processed/"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["!python scripts/train_hf.py --data data/processed/train.jsonl --out /content/outputs/tiny-50M --epochs 1\n", "!ls -lh /content/outputs/tiny-50M"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# push back to Hub (set HF_TOKEN in Colab Secrets)\n", "import os\n", "from huggingface_hub import HfApi\n", "tok = os.getenv('HF_TOKEN')\n", "api = HfApi(token=tok)\n", "api.upload_folder(folder_path='/content/outputs/tiny-50M', repo_id='mrsandip/Alfa-Code', path_in_repo='outputs/tiny-50M')\n", "print('uploaded')"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, "nbformat": 4, "nbformat_minor": 5}