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

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/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

Alfa-Code / scripts /train_tokenizer.py

mrsandip

Make all: data+tiny-50M+gguf-scripts+gradio-UI

d5743c4 verified 15 days ago

Raw History Blame Contribute Delete

1.06 kB

	"""Train BPE tokenizer on data/processed/train.jsonl -> tokenizers/alfa-32k.json"""
	import json
	from pathlib import Path
	from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders

	DATA = Path("data/processed/train.jsonl")
	OUT = Path("tokenizers/alfa-32k.json")

	def texts():
	for line in open(DATA, encoding="utf-8"):
	o = json.loads(line)
	yield o.get("prompt", "") + "\n" + o.get("answer", "")

	def main(vocab_size=32000):
	assert DATA.exists(), f"missing {DATA}, run download_data.py first"
	tok = Tokenizer(models.BPE())
	tok.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
	tok.decoder = decoders.ByteLevel()
	trainer = trainers.BpeTrainer(vocab_size=vocab_size,
	special_tokens=["<s>", "</s>", "<unk>", "<pad>", "<think>", "<image>", "<video>", "<file>", "<code>"])
	tok.train_from_iterator(texts(), trainer=trainer, length=20000)
	OUT.parent.mkdir(parents=True, exist_ok=True)
	tok.save(str(OUT))
	print(f"saved -> {OUT}")

	if __name__ == "__main__":
	main()