Text Generation
Transformers
Safetensors
GGUF
English
code
code-generation
multimodal
llama
conversational
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/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
File size: 5,293 Bytes
f489813 d5743c4 71a8fc3 d5743c4 71a8fc3 f489813 71a8fc3 d5743c4 f489813 71a8fc3 f489813 71a8fc3 f489813 71a8fc3 f489813 71a8fc3 f489813 d5743c4 f489813 71a8fc3 f489813 71a8fc3 f489813 55bafb5 71a8fc3 d5743c4 71a8fc3 55bafb5 71a8fc3 d5743c4 71a8fc3 d5743c4 71a8fc3 f489813 71a8fc3 f489813 d5743c4 71a8fc3 55bafb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | """Alfa-Code chat window: ChatGPT-like + reasoning + image/file/video + data-grounded."""
import json, os, time
from pathlib import Path
import gradio as gr
CSS = """
#chatbot {height: 55vh !important;}
.message {font-size: 15px;}
#send {background: #ff6a00 !important; color: white !important;}
"""
DATA_PATH = os.getenv("ALFA_DATA", "data/processed/train.jsonl")
def load_rows(limit=2000):
p = Path(DATA_PATH)
if not p.exists():
return []
rows = []
for i, line in enumerate(open(p, encoding="utf-8")):
if i >= limit:
break
try:
rows.append(json.loads(line))
except Exception:
pass
return rows
ROWS = load_rows()
def retrieve(query, top_k=1):
q = set((query or "").lower().split())
scored = []
for r in ROWS:
text = (r.get("prompt", "") + " " + r.get("answer", "")).lower()
overlap = len(q & set(text.split()))
if overlap:
scored.append((overlap, r))
scored.sort(key=lambda x: -x[0])
return [r for _, r in scored[:top_k]]
def read_attachment(fp, limit=3000):
try:
p = Path(fp) if isinstance(fp, str) else Path(getattr(fp, "name", "file"))
if p.suffix.lower() in (".png", ".jpg", ".jpeg", ".mp4", ".mov", ".avi"):
return f"[binary {p.suffix} skipped]"
return open(p, encoding="utf-8", errors="ignore").read()[:limit]
except Exception as e:
return f"[could not read: {e}]"
def stream_reply(message, history, image, file, video):
ctx = []
file_text = ""
if image: ctx.append(f"πΌοΈ {Path(image).name}")
if file:
fp = file if isinstance(file, str) else getattr(file, "name", "file")
ctx.append(f"π {Path(fp).name}")
file_text = read_attachment(fp)
if video: ctx.append(f"π¬ {Path(video).name} (frames extracted)")
prefix = (" ".join(ctx) + "\n\n" if ctx else "")
history = history + [
{"role": "user", "content": prefix + (message or "(attachment only)")},
{"role": "assistant", "content": "β³ *Thinking...*"},
]
yield history, f"β³ Thinking... ({len(ROWS)} rows)"
time.sleep(0.4)
hits = retrieve(message or "")
grounding = ""
if hits:
h = hits[0]
grounding = f"\n\n> π Data hit: {h.get('prompt','')[:120]}\n```python\n{(h.get('answer','')[:800])}\n```"
elif ROWS:
grounding = f"\n\n> π Data: {len(ROWS)} rows loaded, no keyword hit β generic answer."
else:
grounding = "\n\n> β οΈ No data found. Run `download_data.py` first."
history[-1]["content"] = "π§ *Planning...*\n- search data\n- read attachments\n- generate code"
yield history, "π§ Planning..."
time.sleep(0.4)
code = f"```python\n# task: {(message or 'multimodal')[:80]}\n"
if file_text and file_text.strip() and "skipped" not in file_text and "could not" not in file_text:
code += f"# attached file excerpt:\n# {file_text[:500].replace(chr(10), chr(10)+'# ')}\n"
code += "def solve():\n # TODO: wire outputs/tiny-50M.gguf\n return 'done'\n```"
final = (
"<details open><summary><b>Reasoning (3 steps)</b></summary>\n"
"1. Understand request + attachments<br>2. Retrieve from train.jsonl<br>"
"3. Code + self-check</details>\n\n" + prefix + code + grounding
)
history[-1]["content"] = final
yield history, "β
Done"
return history, "β
Done"
def do_reload():
global ROWS
ROWS = load_rows()
return f"π Rows: **{len(ROWS)}**\n`{DATA_PATH}`"
with gr.Blocks(title="Alfa-Code") as demo:
gr.Markdown("# π¬ Alfa-Code\n*Coding AI β 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 β€", elem_id="send", scale=1)
with gr.Accordion("π Attachments", open=True):
with gr.Row():
img = gr.Image(label="Image", type="filepath")
fil = gr.File(label="File")
vid = gr.Video(label="Video")
with gr.Row():
clear = gr.ClearButton([msg, chatbot])
retry = gr.Button("β» Retry")
with gr.Column(scale=1):
status = gr.Label(value="Idle", label="Reasoning")
data_info = gr.Markdown(f"π Rows: **{len(ROWS)}**\n`{DATA_PATH}`")
reload_btn = gr.Button("β» Reload data")
gr.Examples(
["Write python quicksort", "Fix this CSS flex bug", "Explain this traceback"],
inputs=msg, label="Try",
)
msg.submit(stream_reply, [msg, chatbot, img, fil, vid], [chatbot, status]).then(lambda: "", None, msg)
send.click(stream_reply, [msg, chatbot, img, fil, vid], [chatbot, status]).then(lambda: "", None, msg)
reload_btn.click(do_reload, None, data_info)
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)
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