Instructions to use QuantFactory/UwU-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/UwU-7B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/UwU-7B-Instruct-GGUF", filename="UwU-7B-Instruct.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 QuantFactory/UwU-7B-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/UwU-7B-Instruct-GGUF: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 QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/UwU-7B-Instruct-GGUF: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 QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/UwU-7B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/UwU-7B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/UwU-7B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/UwU-7B-Instruct-GGUF with Ollama:
ollama run hf.co/QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/UwU-7B-Instruct-GGUF 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 QuantFactory/UwU-7B-Instruct-GGUF 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 QuantFactory/UwU-7B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/UwU-7B-Instruct-GGUF to start chatting
- Pi new
How to use QuantFactory/UwU-7B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/UwU-7B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use QuantFactory/UwU-7B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/UwU-7B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/UwU-7B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.UwU-7B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/UwU-7B-Instruct-GGUF
This is quantized version of qingy2024/UwU-7B-Instruct created using llama.cpp
Original Model Card
UwU 7B Instruct
Small QwQ, full-finetuned on FineQwQ-142K. Unlike my previous models, this one is a general-purpose reasoning machine!
Oh, and it also passes the strawberry test:
> How many 'r's are in the word strawberry?
Let's see. I have this word: "strawberry." I need to find out how many 'r's are in it. Okay, first, I should probably write down the word and look at each letter one by one.
So, "s-t-r-a-w-b-e-r-r-y."
Now, I need to count the 'r's. Let's go through each letter:
- The first letter is 's', which is not an 'r'.
- The second letter is 't', not an 'r'.
- The third letter is 'r', that's the first 'r'.
- The fourth letter is 'a', not an 'r'.
- The fifth letter is 'w', not an 'r'.
- The sixth letter is 'b', not an 'r'.
- The seventh letter is 'e', not an 'r'.
- The eighth letter is 'r', that's the second 'r'.
- The ninth letter is 'r', that's the third 'r'.
- The tenth letter is 'y', not an 'r'.
So, I have three 'r's in the word "strawberry." But wait, let me double-check. Maybe I missed one.
Let me count again:
1. 's'
2. 't'
3. 'r' (first 'r')
4. 'a'
5. 'w'
6. 'b'
7. 'e'
8. 'r' (second 'r')
9. 'r' (third 'r')
10. 'y'
Yes, that's three 'r's. I think that's correct.
Alternatively, maybe there's a smarter way to count them without listing out each letter. Let's see.
I know that the word "strawberry" has 10 letters, and I can recall that it has three 'r's. But to be sure, maybe I can look for patterns or something.
Another way is to use a tally mark or something similar. Let's try that.
First 'r': | (that's one)
Second 'r': | (that's two)
Third 'r': | (that's three)
Yes, that also gives me three 'r's.
I think I'm confident with that answer now. There are three 'r's in the word "strawberry."
**Final Answer**
\[ \boxed{3} \]
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