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 / README.md

mrsandip

docs: code-100k deprecated

9f336b6 verified 4 days ago

preview code

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3.31 kB

	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- code
	- code-generation
	- multimodal
	- gguf
	- llama
	datasets:
	- HuggingFaceH4/CodeAlpaca_20K
	metrics:
	- code_eval
	- perplexity
	version: 0.0.2
	---

	# Alfa Code - Multimodal Coding GGUF

	Llama-based code models, trained on Kaggle T4. Ready-to-run GGUF weights below.

	## Download (ready weights)

	GitHub release: https://github.com/mrsandipmandal/alfa-code/releases/tag/v0.0.2
	HuggingFace: https://huggingface.co/mrsandip/Alfa-Code/tree/main/gguf

	\| Model \| Q8_0 (best) \| Q4_0 (small) \| Context \|
	\|---\|---\|---\|---\|
	\| tiny-50M (56M) \| [tiny-50M-q8.gguf](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/tiny-50M-q8.gguf) (60 MB) \| [tiny-50M-q4.gguf](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/tiny-50M-q4.gguf) (32 MB) \| 2048 \|
	\| base-1B (1.1B) \| [base-1B-q8.gguf](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/base-1B-q8.gguf) (1.19 GB) \| [base-1B-q4.gguf](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/base-1B-q4.gguf) (632 MB) \| 2048 \|
	\| code-100k (1.39B) \| ~~code-100k-Q8_0.gguf~~ \| ~~code-100k-Q4_0.gguf~~ \| — \|

	> code-100k files deprecated (broken tokenizer build, model dropped). Current line: tiny-50M + base-1B (fixed build: faithful HF-identical tokenization, embedded chat template). Next: `alfa-code-1.2` (this branch).

	> code-100k is an early checkpoint (1 epoch, eval loss ~3.1) — loads and runs, but output quality is rough. More training coming.

	```bash
	llama-server -m base-1B-q4.gguf -c 2048
	llama-server -m tiny-50M-q8.gguf -c 2048
	```

	Tokenizer (32k BPE): `tokenizers/alfa-32k.json` — also in the v0.0.2 release assets.

	## 1. Data
	`python scripts/download_data.py --dataset code_small_test --max-rows 500`
	Output: `data/processed/train.jsonl` {modality,prompt,think,answer}

	## 2. Tokenizer + Train
	`python scripts/train_tokenizer.py` -> `tokenizers/alfa-32k.json` (32000 vocab, ~419KB)
	`pip install torch --index-url https://download.pytorch.org/whl/cu121`
	`python scripts/train.py` -> `outputs/tiny-50M`

	Scale-up (Colab T4, ~2-4h): 5000+ rows, chat tuning, 3-5 epochs:
	`python scripts/download_data.py --dataset multimodal_mix --max-rows 5000 --overwrite`
	`python scripts/download_data.py --dataset chat_qa --max-rows 2000` (appends Q&A rows)
	`python scripts/train_hf.py --data data/processed/train.jsonl --out /content/outputs/tiny-50M --epochs 3 --config configs/tiny-50M.yaml --max-seq-len 2048 --batch-size 1`

	1B model (16GB+ GPU only, not RTX 3050 4GB): same commands with `--config configs/base-1B.yaml --out /content/outputs/base-1B` (1.12B params, ~2.2GB fp16).

	## 3. GGUF (no llama.cpp clone needed)
	`pip install gguf safetensors torch transformers`
	`python scripts/convert_to_gguf.py --ckpt outputs/tiny-50M --quant Q8_0` (Q8_0 ~55MB, Q4_0 ~32MB, F16 ~105MB)
	K-quants (Q4_K_M) need standard dims + `llama-quantize` binary — not offered (intermediate 1376).

	Run local (RTX 3050 4GB):
	`llama-server -m outputs/tiny-50M.gguf -c 2048` or Ollama Modelfile (`FROM ./outputs/tiny-50M.gguf`).

	## 4. UI (reasoning live->collapsed + image/file/video)
	`python app/ui.py` -> http://127.0.0.1:7860

	## 5. Hub sync
	`hf upload mrsandip/Alfa-Code . . --exclude __pycache__ --exclude .venv --commit-message "Update"`