Instructions to use mrsandip/Alfa-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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 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/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 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
- 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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrsandip/Alfa-Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrsandip/Alfa-Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrsandip/Alfa-Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - 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
Download scripts/list_datasets.py from mrsandip/Alfa-Code: direct link, hf CLI and curl.
- Browser
- Download file 686 Bytes
-
https://huggingface.co/mrsandip/Alfa-Code/resolve/main/scripts/list_datasets.py
- Command line
-
hf download hf://mrsandip/Alfa-Code/scripts/list_datasets.py
-
curl -L -o list_datasets.py https://huggingface.co/mrsandip/Alfa-Code/resolve/main/scripts/list_datasets.py
686 Bytes
| """Best HuggingFace datasets for multimodal coding model (code+reasoning+image+video).""" | |
| DATASETS = { | |
| # code text - gated large, needs HF_TOKEN | |
| "code_pretrain": "bigcode/the-stack-v2", | |
| # public test - no login needed | |
| "code_small_test": "HuggingFaceH4/CodeAlpaca_20K", | |
| # reasoning / instruction | |
| "code_instruct": "bigcode/self-oss-instruct", | |
| "reasoning_general": "Open-Orca/OpenOrca", | |
| # image -> code (screenshot + html) | |
| "image_to_code": "HuggingFaceM4/websight", | |
| } | |
| print("Recommended HF datasets:") | |
| for k, v in DATASETS.items(): | |
| print(f" {k:18s} -> {v}") | |
| print("\nRun: python scripts/download_data.py --dataset code_small_test --max-rows 500") | |