README.md

---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
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extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  geo: ip_location
  ? By clicking Submit below I accept the terms of the license and acknowledge that
    the information I provide will be collected stored processed and shared in accordance
    with the Meta Privacy Policy
  : checkbox
extra_gated_description: The information you provide will be collected, stored, processed
  and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
- example_title: Hello
  messages:
  - role: user
    content: Hey my name is Julien! How are you?
- example_title: Winter holidays
  messages:
  - role: system
    content: You are a helpful and honest assistant. Please, respond concisely and
      truthfully.
  - role: user
    content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
  messages:
  - role: system
    content: You are a helpful and honest code and programming assistant. Please,
      respond concisely and truthfully.
  - role: user
    content: Write a function that computes the nth fibonacci number.
inference:
  parameters:
    max_new_tokens: 300
    stop:
    - <|end_of_text|>
    - <|eot_id|>
---

# sdkramer10/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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/meta-llama/Meta-Llama-3-8B-Instruct) 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 sdkramer10/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo sdkramer10/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-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 sdkramer10/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo sdkramer10/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -c 2048
```