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 Oct 3

Commit

55bafb5

verified ·

1 Parent(s): 71a8fc3

Fix Gradio6 chat UI

Browse files

Files changed (1) hide show

app/ui.py +3 -3

app/ui.py CHANGED Viewed

@@ -39,11 +39,11 @@ def stream_reply(message, history, image, file, video):
     yield history, "✅ Done"
     return history, "✅ Done"
-with gr.Blocks(title="Alfa-Code", css=CSS, theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 💬 Alfa-Code\n*Coding AI — reasoning + image + file + video*")
     with gr.Row():
         with gr.Column(scale=3):
-            chatbot = gr.Chatbot(label="Chat", elem_id="chatbot", type="messages", show_copy_button=True)
             with gr.Row():
                 msg = gr.Textbox(placeholder="Ask to code... (Enter to send)", show_label=False, scale=4)
                 send = gr.Button("Send ➤", elem_id="send", scale=1)
@@ -67,4 +67,4 @@ with gr.Blocks(title="Alfa-Code", css=CSS, theme=gr.themes.Soft()) as demo:
 if __name__ == "__main__":
     import sys
     port = int(sys.argv[1]) if len(sys.argv) > 1 else 7861
-    demo.launch(server_name="127.0.0.1", server_port=port)

     yield history, "✅ Done"
     return history, "✅ Done"
+with gr.Blocks(title="Alfa-Code") as demo:
     gr.Markdown("# 💬 Alfa-Code\n*Coding AI — reasoning + image + file + video*")
     with gr.Row():
         with gr.Column(scale=3):
+            chatbot = gr.Chatbot(label="Chat", elem_id="chatbot", height=500, buttons=["copy"])
             with gr.Row():
                 msg = gr.Textbox(placeholder="Ask to code... (Enter to send)", show_label=False, scale=4)
                 send = gr.Button("Send ➤", elem_id="send", scale=1)
 if __name__ == "__main__":
     import sys
     port = int(sys.argv[1]) if len(sys.argv) > 1 else 7861
+    demo.launch(server_name="127.0.0.1", server_port=port, theme=gr.themes.Soft(), css=CSS)