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 20 days ago

Commit

3e98a04

verified ·

1 Parent(s): 3b489c0

docs: code-100k GGUF links

Browse files

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -31,9 +31,10 @@ Llama-based code models, trained on Kaggle T4. Ready-to-run GGUF weights below.
 |---|---|---|---|
 | 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) | ⏳ Kaggle upload pending | ⏳ | 8192 (YaRN → 131k) |
 ```bash
 llama-server -m base-1B-q4.gguf -c 2048
 llama-server -m tiny-50M-q8.gguf -c 2048
 ```

 |---|---|---|---|
 | 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](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/code-100k-Q8_0.gguf) (1.5 GB) | [code-100k-Q4_0.gguf](https://huggingface.co/mrsandip/Alfa-Code/resolve/main/gguf/code-100k-Q4_0.gguf) (786 MB) | 8192 (YaRN → 131k) |
 ```bash
+llama-server -m code-100k-Q4_0.gguf -c 8192   # 1.39B, best model
 llama-server -m base-1B-q4.gguf -c 2048
 llama-server -m tiny-50M-q8.gguf -c 2048
 ```