Instructions to use PyThaGo/LeetSeek-R1ML32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PyThaGo/LeetSeek-R1ML32B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PyThaGo/LeetSeek-R1ML32B",
	filename="LitSeek-R1ML-32B.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PyThaGo/LeetSeek-R1ML32B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PyThaGo/LeetSeek-R1ML32B
# Run inference directly in the terminal:
llama-cli -hf PyThaGo/LeetSeek-R1ML32B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PyThaGo/LeetSeek-R1ML32B
# Run inference directly in the terminal:
llama-cli -hf PyThaGo/LeetSeek-R1ML32B

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 PyThaGo/LeetSeek-R1ML32B
# Run inference directly in the terminal:
./llama-cli -hf PyThaGo/LeetSeek-R1ML32B

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 PyThaGo/LeetSeek-R1ML32B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PyThaGo/LeetSeek-R1ML32B

Use Docker

docker model run hf.co/PyThaGo/LeetSeek-R1ML32B

LM Studio
Jan
Ollama
How to use PyThaGo/LeetSeek-R1ML32B with Ollama:
```
ollama run hf.co/PyThaGo/LeetSeek-R1ML32B
```

Unsloth Studio new

How to use PyThaGo/LeetSeek-R1ML32B 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 PyThaGo/LeetSeek-R1ML32B 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 PyThaGo/LeetSeek-R1ML32B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PyThaGo/LeetSeek-R1ML32B to start chatting

Docker Model Runner
How to use PyThaGo/LeetSeek-R1ML32B with Docker Model Runner:
```
docker model run hf.co/PyThaGo/LeetSeek-R1ML32B
```

Lemonade

How to use PyThaGo/LeetSeek-R1ML32B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PyThaGo/LeetSeek-R1ML32B

Run and chat with the model

lemonade run user.LeetSeek-R1ML32B-{{QUANT_TAG}}

List all available models

lemonade list

Cristian Sas commited on Feb 7, 2025

Commit

5d4dc7e

verified ·

1 Parent(s): 98cad72

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -88,8 +88,8 @@ Pentru a utiliza LLMLit, instalează librăriile necesare și încarcă modelul:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # Încarcă modelul și tokenizer-ul
-model = AutoModelForCausalLM.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
 # Generează text
 inputs = tokenizer("Your prompt here", return_tensors="pt")

 from transformers import AutoModelForCausalLM, AutoTokenizer
 # Încarcă modelul și tokenizer-ul
+model = AutoModelForCausalLM.from_pretrained("llmlit/LitSeek-R1ML-32B")
+tokenizer = AutoTokenizer.from_pretrained("llmlit/LitSeek-R1ML-32B")
 # Generează text
 inputs = tokenizer("Your prompt here", return_tensors="pt")