Instructions to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="erenyeager-1/flan-t5-large-Q4_K_S-GGUF", filename="flan-t5-large-q4_k_s.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF 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 erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
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 erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
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 erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
Use Docker
docker model run hf.co/erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF with Ollama:
ollama run hf.co/erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
- Unsloth Studio
How to use erenyeager-1/flan-t5-large-Q4_K_S-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 erenyeager-1/flan-t5-large-Q4_K_S-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 erenyeager-1/flan-t5-large-Q4_K_S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for erenyeager-1/flan-t5-large-Q4_K_S-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF with Docker Model Runner:
docker model run hf.co/erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
- Lemonade
How to use erenyeager-1/flan-t5-large-Q4_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull erenyeager-1/flan-t5-large-Q4_K_S-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.flan-t5-large-Q4_K_S-GGUF-Q4_K_S
List all available models
lemonade list
| language: | |
| - en | |
| - fr | |
| - ro | |
| - de | |
| - multilingual | |
| widget: | |
| - text: 'Translate to German: My name is Arthur' | |
| example_title: Translation | |
| - text: Please answer to the following question. Who is going to be the next Ballon | |
| d'or? | |
| example_title: Question Answering | |
| - text: 'Q: Can Geoffrey Hinton have a conversation with George Washington? Give the | |
| rationale before answering.' | |
| example_title: Logical reasoning | |
| - text: Please answer the following question. What is the boiling point of Nitrogen? | |
| example_title: Scientific knowledge | |
| - text: Answer the following yes/no question. Can you write a whole Haiku in a single | |
| tweet? | |
| example_title: Yes/no question | |
| - text: Answer the following yes/no question by reasoning step-by-step. Can you write | |
| a whole Haiku in a single tweet? | |
| example_title: Reasoning task | |
| - text: 'Q: ( False or not False or False ) is? A: Let''s think step by step' | |
| example_title: Boolean Expressions | |
| - text: The square root of x is the cube root of y. What is y to the power of 2, if | |
| x = 4? | |
| example_title: Math reasoning | |
| - text: 'Premise: At my age you will probably have learnt one lesson. Hypothesis: It''s | |
| not certain how many lessons you''ll learn by your thirties. Does the premise | |
| entail the hypothesis?' | |
| example_title: Premise and hypothesis | |
| tags: | |
| - text2text-generation | |
| - llama-cpp | |
| - gguf-my-repo | |
| datasets: | |
| - svakulenk0/qrecc | |
| - taskmaster2 | |
| - djaym7/wiki_dialog | |
| - deepmind/code_contests | |
| - lambada | |
| - gsm8k | |
| - aqua_rat | |
| - esnli | |
| - quasc | |
| - qed | |
| license: apache-2.0 | |
| base_model: google/flan-t5-large | |
| # erenyeager-1/flan-t5-large-Q4_K_S-GGUF | |
| This model was converted to GGUF format from [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. | |
| Refer to the [original model card](https://huggingface.co/google/flan-t5-large) for more details on the model. | |
| ## Use with llama.cpp | |
| Install llama.cpp through brew (works on Mac and Linux) | |
| ```bash | |
| brew install llama.cpp | |
| ``` | |
| Invoke the llama.cpp server or the CLI. | |
| ### CLI: | |
| ```bash | |
| llama-cli --hf-repo erenyeager-1/flan-t5-large-Q4_K_S-GGUF --hf-file flan-t5-large-q4_k_s.gguf -p "The meaning to life and the universe is" | |
| ``` | |
| ### Server: | |
| ```bash | |
| llama-server --hf-repo erenyeager-1/flan-t5-large-Q4_K_S-GGUF --hf-file flan-t5-large-q4_k_s.gguf -c 2048 | |
| ``` | |
| Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. | |
| Step 1: Clone llama.cpp from GitHub. | |
| ``` | |
| git clone https://github.com/ggerganov/llama.cpp | |
| ``` | |
| Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). | |
| ``` | |
| cd llama.cpp && LLAMA_CURL=1 make | |
| ``` | |
| Step 3: Run inference through the main binary. | |
| ``` | |
| ./llama-cli --hf-repo erenyeager-1/flan-t5-large-Q4_K_S-GGUF --hf-file flan-t5-large-q4_k_s.gguf -p "The meaning to life and the universe is" | |
| ``` | |
| or | |
| ``` | |
| ./llama-server --hf-repo erenyeager-1/flan-t5-large-Q4_K_S-GGUF --hf-file flan-t5-large-q4_k_s.gguf -c 2048 | |
| ``` | |