Instructions to use PRiyanshu0-1/DPDP-SSense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PRiyanshu0-1/DPDP-SSense with Transformers:

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

pipe = pipeline("text-generation", model="PRiyanshu0-1/DPDP-SSense")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PRiyanshu0-1/DPDP-SSense", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PRiyanshu0-1/DPDP-SSense 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 PRiyanshu0-1/DPDP-SSense:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PRiyanshu0-1/DPDP-SSense:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PRiyanshu0-1/DPDP-SSense:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PRiyanshu0-1/DPDP-SSense:Q4_K_M

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 PRiyanshu0-1/DPDP-SSense:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PRiyanshu0-1/DPDP-SSense:Q4_K_M

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 PRiyanshu0-1/DPDP-SSense:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PRiyanshu0-1/DPDP-SSense:Q4_K_M

Use Docker

docker model run hf.co/PRiyanshu0-1/DPDP-SSense:Q4_K_M

LM Studio
Jan

vLLM

How to use PRiyanshu0-1/DPDP-SSense with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PRiyanshu0-1/DPDP-SSense"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PRiyanshu0-1/DPDP-SSense",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PRiyanshu0-1/DPDP-SSense:Q4_K_M

SGLang

How to use PRiyanshu0-1/DPDP-SSense 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 "PRiyanshu0-1/DPDP-SSense" \
    --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": "PRiyanshu0-1/DPDP-SSense",
		"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 "PRiyanshu0-1/DPDP-SSense" \
        --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": "PRiyanshu0-1/DPDP-SSense",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use PRiyanshu0-1/DPDP-SSense with Ollama:
```
ollama run hf.co/PRiyanshu0-1/DPDP-SSense:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use PRiyanshu0-1/DPDP-SSense with Docker Model Runner:
```
docker model run hf.co/PRiyanshu0-1/DPDP-SSense:Q4_K_M
```

Lemonade

How to use PRiyanshu0-1/DPDP-SSense with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PRiyanshu0-1/DPDP-SSense:Q4_K_M

Run and chat with the model

lemonade run user.DPDP-SSense-Q4_K_M

List all available models

lemonade list

Atomic Chat

DPDP-SSense / scripts /install.sh

PRiyanshu0-1

Initial push: DPDP SLM Pipeline with comprehensive LFS

1f52672 2 months ago

Raw History Blame Contribute Delete

4.31 kB

	#!/usr/bin/env bash
	# ==============================================================================
	# install.sh – DGX Spark (GB10 / aarch64 / CUDA 13.0) Provisioning Engine
	# ==============================================================================

	set -euo pipefail

	SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
	ML_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
	REPO_ROOT="$(cd "${ML_DIR}/.." && pwd)"

	echo "════════════════════════════════════════════════════════════════════════"
	echo "🚀 INITIATING GB10 HARDWARE-SPECIFIC ML PROVISIONING"
	echo "════════════════════════════════════════════════════════════════════════"
	echo " • ML Root: ${ML_DIR}"
	echo " • Repository Root: ${REPO_ROOT}"

	# 1. CRITICAL COMPILER FLAGS FOR BLACKWELL (sm_121) ON CUDA 13.0
	export TORCH_CUDA_ARCH_LIST="12.0"
	export FLASH_ATTENTION_FORCE_BUILD="TRUE"
	export XFORMERS_MORE_DETAILS=1

	echo "✅ Environment variables configured: TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}"

	# ==============================================================================
	# PHASE 1: Build Essentials & Base Requirements
	# ==============================================================================
	echo -e "\n📦 [PHASE 1] Upgrading pip, installing build essentials & requirements..."
	python3 -m pip install --upgrade pip wheel setuptools packaging ninja cmake

	# Robustly installs requirements.txt from ml directory
	python3 -m pip install -r "${ML_DIR}/requirements.txt"

	# ==============================================================================
	# PHASE 2: Xformers (Source Build for aarch64 + CUDA 13.0)
	# ==============================================================================
	echo -e "\n🔥 [PHASE 2] Compiling Xformers from source (8 Threads)..."
	export MAX_JOBS=8
	python3 -m pip install -v -U git+https://github.com/facebookresearch/xformers.git@v0.0.30#egg=xformers --no-build-isolation

	# ==============================================================================
	# PHASE 3: Flash-Attention (Source Build for aarch64 + CUDA 13.0)
	# ==============================================================================
	echo -e "\n⚡ [PHASE 3] Compiling Flash-Attention (RAM THROTTLED to 4 Threads)..."
	export MAX_JOBS=4
	python3 -m pip install -v -U git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3#egg=flash-attn --no-build-isolation

	# ==============================================================================
	# PHASE 4: Llama-CPP-Python (GGUF Export Backend)
	# ==============================================================================
	echo -e "\n🦙 [PHASE 4] Compiling Llama-CPP-Python with CUDA 13.0 backend..."
	CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=120" \
	python3 -m pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir

	# ==============================================================================
	# PHASE 5: Unsloth, Unsloth Zoo & BitsAndBytes (IRONCLAD NO-DEPS)
	# ==============================================================================
	echo -e "\n🦥 [PHASE 5] Installing Unsloth + Unsloth Zoo & BitsAndBytes..."

	# Official aarch64 wheel with strict no-deps
	python3 -m pip install "bitsandbytes>=0.50.0" --no-deps

	python3 -c "import bitsandbytes" \|\| echo "⚠️ bitsandbytes native library notice -- proceeding with adamw_torch optimizer"

	# Hard-pinned to Unsloth's exact caps
	python3 -m pip install --upgrade --force-reinstall --no-cache-dir --no-deps \
	"unsloth==2026.8.15" "unsloth_zoo==2026.8.10"

	echo -e "\n════════════════════════════════════════════════════════════════════════"
	echo "✅ SUCCESS: GB10 aarch64 Provisioning Complete!"
	echo "════════════════════════════════════════════════════════════════════════"