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 Aug 26, 2025

Commit

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-# 169Pi/Alpie-core
-## Model Summary
-`169Pi/Alpie-core` is a 32B parameter causal language model.
-It is the **world’s first large-scale 4-bit LoRA-trained model**, optimized over **three distinct training phases** for reasoning, knowledge integration, and benchmark performance.
-The model specializes in **mathematics, coding, science, competitive exams, Indian context, and law**.
----
-## Model Details
-- **Base Model:** `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`
-- **Architecture:** 32B parameter causal LM (chat-optimized)
-- **Quantization:** 4-bit NF4 with double quantization enabled
-- **Precision for Inference:** 4-bit NF4
-- **Frameworks:** PEFT, LoRA, bitsandbytes, PyTorch
-- **Max Context Length:** 65k tokens
-- **Deployment Framework:** vLLM
-- **License:** *(to be filled)*
----
----
-## Hyperparameters
-- **Epochs per phase:** 2
-- **Batch Size:** 256
-- **Gradient Accumulation Steps:** 4
-- **Learning Rate:** `1e-5` (initially `2e-5`, reduced to avoid early over-generalization)
-- **Scheduler:** Cosine
-- **Optimizer:** AdamW (`adamw_torch`)
-- **LoRA Rank (r):** 16
-- **LoRA Alpha:** 8
-- **LoRA Dropout:** 0.1
-- **Target Modules:** `q_proj`, `v_proj`
----
-## Intended Use
-- **Primary:** Educational tutoring, competitive exam preparation, coding assistance, legal reasoning, general knowledge Q&A.
-- **Secondary:** Research support, problem-solving in science and mathematics.
----
-## Limitations & Warnings
-- May produce inaccurate or outdated information for highly recent events.
-- Not suitable for tasks requiring legal or medical advice without expert review.
-- Performance may vary outside trained domains.
----
-## Citation
-If you use this model in your research, please cite: