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

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

9c2601f

verified ·

1 Parent(s): 52b4b37

Update README.md

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@@ -1,11 +1,8 @@
 # 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]*
 ---
@@ -22,7 +19,7 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 - **Context Length**: 65,536 tokens
 - **Max Output Length**: 16,384 tokens
 - **License**: Apache 2.0
 ## 3. Model Features
@@ -50,11 +47,11 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 | Benchmark | Alpie-Core (32B-4bit) | DeepSeek-V2 (236B) | Qwen2.5 72B | Llama 3.1 405B | Llama 3.1 70B | Gemma-3 27B-PT | Mistral-Small-24B-Base-2501 |
 |-----------|----------------------|-------------------|-------------|---------------|---------------|----------------|----------------------------|
 | MMLU (5-shot) | **81.28%** | 78.4% | 85.0% | 84.4% | 79.3% | 78.6% | 80.73% |
-| GSM8K (8-shot) | **92.75%** | 81.6% | 88.3% | 83.5% | - | 82.2% | 80.73% |
-| BBH (3-shot) | **85.12%** | 78.8% | 79.8% | 82.9% | 81.6% | 77.7% | - |
 | MMLU-Pro (5-shot) | **64.78%** | 51.4% | 58.3% | 52.8% | 53.8% | 52.2% | 54.37% |
-| MBPP (pass@1) | **75.20%** | 65.0% | 72.6% | 68.4% | - | 65.6% | 69.64% |
-| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | - | 48.8% | - |
 ### SWE-Bench Verified Performance

 # Alpie-Core: 4-bit Quantized Reasoning Model
 ---
+*[Space reserved for blog paper, technical report links, and company logo]*
 ---
 - **Context Length**: 65,536 tokens
 - **Max Output Length**: 16,384 tokens
 - **License**: Apache 2.0
+- **Memory Footprint**: ~8GB (75% reduction from full-precision)
 ## 3. Model Features
 | Benchmark | Alpie-Core (32B-4bit) | DeepSeek-V2 (236B) | Qwen2.5 72B | Llama 3.1 405B | Llama 3.1 70B | Gemma-3 27B-PT | Mistral-Small-24B-Base-2501 |
 |-----------|----------------------|-------------------|-------------|---------------|---------------|----------------|----------------------------|
 | MMLU (5-shot) | **81.28%** | 78.4% | 85.0% | 84.4% | 79.3% | 78.6% | 80.73% |
+| GSM8K (8-shot) | **92.75%** | 81.6% | 88.3% | 83.5% | nan | 82.2% | 80.73% |
+| BBH (3-shot) | **85.12%** | 78.8% | 79.8% | 82.9% | 81.6% | 77.7% | nan |
 | MMLU-Pro (5-shot) | **64.78%** | 51.4% | 58.3% | 52.8% | 53.8% | 52.2% | 54.37% |
+| MBPP (pass@1) | **75.20%** | 65.0% | 72.6% | 68.4% | nan | 65.6% | 69.64% |
+| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | nan | 48.8% | nan |
 ### SWE-Bench Verified Performance