metadata

language:
  - sv
  - en
  - 'no'
  - da
license: mit
tags:
  - pretrained
  - flashback
  - web
  - conversational
base_model: meta-llama/Meta-Llama-3-8B
pipeline_tag: text-generation
widget:
  - text: Jag tycker att det är roligt med

🦙 Llama-3-8B-flashback-v1-Q5_K_M-GGUF

Llama-3-8B-flashback-v1 is a continuation of the pretraining process for the base meta-llama/Meta-Llama-3-8B model, utilizing 2 251 233 forum threads from the Swedish website https://www.flashback.org/. Which is rougly 40GB of text. It is a full finetune for three epochs.

catbash/Llama-3-8B-flashback-v1-Q5_K_M-GGUF

This model was converted to GGUF format from timpal0l/Llama-3-8B-flashback-v1 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo catbash/Llama-3-8B-flashback-v1-Q5_K_M-GGUF --hf-file llama-3-8b-flashback-v1-q5_k_m-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo catbash/Llama-3-8B-flashback-v1-Q5_K_M-GGUF --hf-file llama-3-8b-flashback-v1-q5_k_m-imat.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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 catbash/Llama-3-8B-flashback-v1-Q5_K_M-GGUF --hf-file llama-3-8b-flashback-v1-q5_k_m-imat.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo catbash/Llama-3-8B-flashback-v1-Q5_K_M-GGUF --hf-file llama-3-8b-flashback-v1-q5_k_m-imat.gguf -c 2048