metadata

library_name: transformers
license: apache-2.0
language:
  - en
  - zh
  - es
  - de
  - ar
  - ru
  - ja
  - ko
  - hi
  - sk
  - vi
  - tr
  - fi
  - id
  - fa
  - 'no'
  - th
  - sv
  - pt
  - da
  - bn
  - te
  - ro
  - it
  - fr
  - nl
  - sw
  - pl
  - hu
  - cs
  - el
  - uk
  - mr
  - ta
  - tl
  - bg
  - lt
  - ur
  - he
  - gu
  - kn
  - am
  - kk
  - hr
  - uz
  - jv
  - ca
  - az
  - ms
  - sr
  - sl
  - yo
  - lv
  - is
  - ha
  - ka
  - et
  - bs
  - hy
  - ml
  - pa
  - mt
  - km
  - sq
  - or
  - as
  - my
  - mn
  - af
  - be
  - ga
  - mk
  - cy
  - gl
  - ceb
  - la
  - yi
  - lb
  - tg
  - gd
  - ne
  - ps
  - eu
  - ky
  - ku
  - si
  - ht
  - eo
  - lo
  - fy
  - sd
  - mg
  - so
  - ckb
  - su
  - nn
datasets:
  - lightblue/reranker_continuous_filt_max7_train
base_model: lightblue/lb-reranker-0.5B-v1.0
pipeline_tag: text-generation
tags:
  - reranker
  - llama-cpp
  - gguf-my-repo
widget:
  - text: |-
      <<<Query>>>
      How many languages has LB-Reranker been trained on?


      <<<Context>>>
      LB-Reranker has been trained on more than 95 languages.
    example_title: Positive example (7/7)
  - text: |-
      <<<Query>>>
      How many languages has LB-Reranker been trained on?


      <<<Context>>>
      AA-Reranker is applicable to a broad range of use cases.
    example_title: Negative example (2/7)

jimi0209/lb-reranker-0.5B-v1.0-Q5_K_M-GGUF

This model was converted to GGUF format from lightblue/lb-reranker-0.5B-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 jimi0209/lb-reranker-0.5B-v1.0-Q5_K_M-GGUF --hf-file lb-reranker-0.5b-v1.0-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo jimi0209/lb-reranker-0.5B-v1.0-Q5_K_M-GGUF --hf-file lb-reranker-0.5b-v1.0-q5_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 jimi0209/lb-reranker-0.5B-v1.0-Q5_K_M-GGUF --hf-file lb-reranker-0.5b-v1.0-q5_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo jimi0209/lb-reranker-0.5B-v1.0-Q5_K_M-GGUF --hf-file lb-reranker-0.5b-v1.0-q5_k_m.gguf -c 2048