3 min to read
DeepScaleR-1.5B-Preview represents a fully open-source, 1.5 billion-parameter transformer model, optimized through Reinforcement Learning (RL) to surpass OpenAI's o1-preview in mathematical reasoning tasks.
Prior to installation, verify that your computational environment satisfies the following specifications:
git
(for repository management)make
(build automation tool)pip
llama.cpp
llama.cpp
facilitates the efficient execution of large-scale language models.
For macOS and Linux (via Homebrew):
brew install llama.cpp
Manual Compilation:
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
Compiling with Hardware-Specific Optimizations:
LLAMA_CUDA=1 make # NVIDIA GPU acceleration
LLAMA_ROCM=1 make # AMD ROCm support
LLAMA_OPENCL=1 make # OpenCL optimization
LLAMA_AVX512=1 make # AVX-512 instruction set
Obtain the GGUF-formatted model from Hugging Face:
wget https://huggingface.co/NikolayKozloff/DeepScaleR-1.5B-Preview-Q8_0-GGUF/resolve/main/deepscaler-1.5b-preview-q8_0.gguf
llama.cpp
Command-Line Interface (CLI) Execution:
./llama-cli --model deepscaler-1.5b-preview-q8_0.gguf -p "Derive the Taylor series expansion for sin(x)"
Server Deployment:
./llama-server --model deepscaler-1.5b-preview-q8_0.gguf -c 2048
To query the server via API:
curl http://localhost:8080/completion -H "Content-Type: application/json" -d '{
"prompt": "Explain the laws of thermodynamics",
"n_predict": 128
}'
Ollama Installation:
curl -fsSL https://ollama.com/install.sh | sh
Executing DeepScaleR within Ollama:
ollama run deepscaler-1.5b-preview-q8_0.gguf
import requests
def analyze_sentiment(prompt):
url = "http://localhost:8080/completion"
data = {"prompt": prompt, "n_predict": 128}
response = requests.post(url, json=data)
return response.json()
print(analyze_sentiment("The economic outlook appears promising."))
import requests
def generate_code(prompt):
url = "http://localhost:8080/completion"
data = {"prompt": prompt, "n_predict": 128}
response = requests.post(url, json=data)
return response.json()
print(generate_code("Implement a binary search algorithm in Python."))
import requests
def chatbot_response(user_input):
url = "http://localhost:8080/completion"
data = {"prompt": user_input, "n_predict": 128}
response = requests.post(url, json=data)
return response.json()
while True:
user_input = input("User: ")
if user_input.lower() == "exit":
break
response = chatbot_response(user_input)
print("Bot:", response)
-c
values to align with system capabilities.-t
parameter for parallel processing.DeepScaleR 1.5B is a state-of-the-art transformer model that can be seamlessly deployed on Linux platforms via llama.cpp
and Ollama.
By adhering to this comprehensive setup and optimization guide, researchers and engineers can harness DeepScaleR’s capabilities for advanced natural language processing applications, ensuring high-performance inference and computational efficiency.
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