Instructions to use 169Pi/Alpie-Core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 169Pi/Alpie-Core with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="169Pi/Alpie-Core")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("169Pi/Alpie-Core")
model = AutoModelForCausalLM.from_pretrained("169Pi/Alpie-Core")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 169Pi/Alpie-Core with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "169Pi/Alpie-Core"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "169Pi/Alpie-Core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/169Pi/Alpie-Core

SGLang

How to use 169Pi/Alpie-Core 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 "169Pi/Alpie-Core" \
    --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": "169Pi/Alpie-Core",
		"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 "169Pi/Alpie-Core" \
        --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": "169Pi/Alpie-Core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 169Pi/Alpie-Core with Docker Model Runner:
```
docker model run hf.co/169Pi/Alpie-Core
```

deepanshupillm commited on Sep 5, 2025

Commit

861a266

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1 Parent(s): 9c2601f

Update README.md

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@@ -1,9 +1,27 @@
 # Alpie-Core: 4-bit Quantized Reasoning Model
 ---
-*[Space reserved for blog paper, technical report links, and company logo]*
 ---
 ## 1. Introduction
@@ -110,23 +128,8 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 ## 8. Use Cases
-### Scientific Research Excellence
-- 98% performance on SciQ benchmark
-- Advanced physics, chemistry, and mathematical sciences
-- Literature review automation and hypothesis generation
-- Experimental design optimization
-### Advanced Coding and Software Engineering
-- 57.8% SWE-Bench Verified score (8% above nearest competitor)
-- Automated bug detection and GitHub issue resolution
-- Competitive programming and algorithm design
-- Enterprise software development and architecture design
-### Indian Cultural and Religious Expertise
-- Comprehensive understanding of Hindu philosophy, Buddhist traditions
-- Regional diversity and cultural knowledge across Indian states
-- Legal and constitutional framework understanding
-- Educational support for Indian competitive exams (JEE, NEET, UPSC, SSC)
 ## 9. Safety and Limitations

+---
+tags:
+- text-generation
+- reasoning
+- coding
+- mathematics
+- quantization
+license: apache-2.0
+datasets:
+- synthetic
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+language:
+- en
+- hi
+library_name: transformers
+pipeline_tag: text-generation
+---
 # Alpie-Core: 4-bit Quantized Reasoning Model
 ---
+<p align="center">
+  <img src="./Frame%202018777151.png" alt="Alpie-Core Architecture" width="700"/>
+</p>
+*[Space reserved for blog paper, technical report links]*
 ---
 ## 1. Introduction
 ## 8. Use Cases
+Best for **STEM, complex mathematical reasoning, coding, and Indian context**.
 ## 9. Safety and Limitations