metadata
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
- OpenAssistant/oasst1
- databricks/databricks-dolly-15k
widget:
- text: >-
<human>: Write an email to my friends inviting them to come to my home on
Friday for a dinner party, bring their own food to share.
<bot>:
example_title: Email Writing
- text: |-
<human>: Create a list of things to do in San Francisco
<bot>:
example_title: Brainstorming
inference:
parameters:
temperature: 0.7
top_p: 0.7
top_k: 50
max_new_tokens: 128
tags:
- llama-cpp
- gguf-my-repo
base_model: togethercomputer/RedPajama-INCITE-Chat-3B-v1
UKPMAN0/RedPajama-INCITE-Chat-3B-v1-Q4_K_M-GGUF
This model was converted to GGUF format from togethercomputer/RedPajama-INCITE-Chat-3B-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 UKPMAN0/RedPajama-INCITE-Chat-3B-v1-Q4_K_M-GGUF --hf-file redpajama-incite-chat-3b-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo UKPMAN0/RedPajama-INCITE-Chat-3B-v1-Q4_K_M-GGUF --hf-file redpajama-incite-chat-3b-v1-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 UKPMAN0/RedPajama-INCITE-Chat-3B-v1-Q4_K_M-GGUF --hf-file redpajama-incite-chat-3b-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo UKPMAN0/RedPajama-INCITE-Chat-3B-v1-Q4_K_M-GGUF --hf-file redpajama-incite-chat-3b-v1-q4_k_m.gguf -c 2048