---
base_model: google/flan-t5-large
datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
language:
- en
- fr
- ro
- de
- multilingual
license: apache-2.0
tags:
- text2text-generation
- llama-cpp
- gguf-my-repo
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
---

# tianlp/flan-t5-large-Q4_K_M-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 tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.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 tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -c 2048
```