Instructions to use cortexso/deepscaler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/deepscaler with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/deepscaler", filename="deepscaler-1.5b-preview-q2_k.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cortexso/deepscaler with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/deepscaler:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/deepscaler:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/deepscaler:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/deepscaler: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 cortexso/deepscaler:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/deepscaler: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 cortexso/deepscaler:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/deepscaler:Q4_K_M
Use Docker
docker model run hf.co/cortexso/deepscaler:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/deepscaler with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/deepscaler" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/deepscaler", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/deepscaler:Q4_K_M
- Ollama
How to use cortexso/deepscaler with Ollama:
ollama run hf.co/cortexso/deepscaler:Q4_K_M
- Unsloth Studio new
How to use cortexso/deepscaler with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/deepscaler to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/deepscaler to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/deepscaler to start chatting
- Docker Model Runner
How to use cortexso/deepscaler with Docker Model Runner:
docker model run hf.co/cortexso/deepscaler:Q4_K_M
- Lemonade
How to use cortexso/deepscaler with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/deepscaler:Q4_K_M
Run and chat with the model
lemonade run user.deepscaler-Q4_K_M
List all available models
lemonade list
File size: 865 Bytes
a93e39d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # BEGIN GENERAL GGUF METADATA
id: deepscaler
model: deepscaler
name: deepscaler
version: 1
# END GENERAL GGUF METADATA
# BEGIN INFERENCE PARAMETERS
# BEGIN REQUIRED
stop:
- <|im_end|>
# END REQUIRED
# BEGIN OPTIONAL
stream: true
top_p: 0.9
temperature: 0.7
frequency_penalty: 0
presence_penalty: 0
max_tokens: 4096
seed: -1
dynatemp_range: 0
dynatemp_exponent: 1
top_k: 40
min_p: 0.05
tfs_z: 1
typ_p: 1
repeat_last_n: 64
repeat_penalty: 1
mirostat: false
mirostat_tau: 5
mirostat_eta: 0.100000001
penalize_nl: false
ignore_eos: false
n_probs: 0
min_keep: 0
# END OPTIONAL
# END INFERENCE PARAMETERS
# BEGIN MODEL LOAD PARAMETERS
# BEGIN REQUIRED
engine: llama-cpp
prompt_template: |
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
ctx_len: 4096
ngl: 34
# END REQUIRED
# END MODEL LOAD PARAMETERS
|