metadata

license: apache-2.0
datasets:
  - cerebras/SlimPajama-627B
  - bigcode/starcoderdata
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/ultrafeedback_binarized
language:
  - en
widget:
  - example_title: Fibonacci (Python)
    messages:
      - role: system
        content: You are a chatbot who can help code!
      - role: user
        content: >-
          Write me a function to calculate the first 10 digits of the fibonacci
          sequence in Python and print it out to the CLI.
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
  - llama-cpp
  - gguf-my-repo

Krizen/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF

This model was converted to GGUF format from TinyLlama/TinyLlama-1.1B-Chat-v1.0 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 Krizen/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --hf-file tinyllama-1.1b-chat-v1.0-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Krizen/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --hf-file tinyllama-1.1b-chat-v1.0-q4_k_m.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 Krizen/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --hf-file tinyllama-1.1b-chat-v1.0-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Krizen/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --hf-file tinyllama-1.1b-chat-v1.0-q4_k_m.gguf -c 2048