cosmopolitan/third_party/radpajama
Justine Tunney 0409096658
Get us closer to building busybox
This change undefines __linux__ and adds APIs like clock_settime(). The
gosh darned getopt_long() API has been reintroduced, thanks to OpenBSD.
2023-06-18 04:13:45 -07:00
..
scripts Get radpajama to build 2023-05-13 20:44:36 -07:00
common-gptneox.cc Get radpajama to build 2023-05-13 20:44:36 -07:00
common-gptneox.h Make more ML improvements 2023-05-16 08:07:23 -07:00
copy-gptneox.cc Make changes needed for new demo 2023-06-15 23:22:49 -07:00
gptneox-util.h Get us closer to building busybox 2023-06-18 04:13:45 -07:00
gptneox.cc Make more ML improvements 2023-05-16 08:07:23 -07:00
gptneox.h Make more ML improvements 2023-05-16 08:07:23 -07:00
LICENSE Import radpajama (a redpajama.cpp fork) (#814) 2023-05-11 07:12:08 -07:00
main-redpajama-chat.cc Make changes needed for new demo 2023-06-15 23:22:49 -07:00
main-redpajama.cc Make changes needed for new demo 2023-06-15 23:22:49 -07:00
quantize-gptneox.cc Make changes needed for new demo 2023-06-15 23:22:49 -07:00
radpajama.mk Clean up more code 2023-06-18 01:00:05 -07:00
README.cosmo Import radpajama (a redpajama.cpp fork) (#814) 2023-05-11 07:12:08 -07:00
README.md Get radpajama to build 2023-05-13 20:44:36 -07:00

gglm Support for RedPajama Model

Ackonwledgement

We highly appreciate the great effort from the fork of gptneox.cpp. Our support of the RedPajama Model is mainly based on this implementation. We extend the model configure and fixed a bug when setting use_parallel_residual flag to False in their original implementation. We also extend the chat model for RedPajama.

Usage:

RedPajama Chat model:

  • Make the code:

      make redpajama-chat quantize-gptneox
    
  • Prepare the RedPajama model (f16 and q4_0) for gglm:

      bash ./examples/redpajama/scripts/install-RedPajama-INCITE-Chat-3B-v1.sh
    
  • Run RedPajama chat model (fp16):

      ./redpajama-chat -m ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Chat-3B-v1-f16.bin \
      -c 2048 \
      -b 128 \
      -n 1 \
      -t 8 \
      --instruct \
      --color \
      --top_k 30 \
      --top_p 0.95 \
      --temp 0.8 \
      --repeat_last_n 3 \
      --repeat_penalty 1.1 \
      --seed 0
    

    Note that you may need to install torch and transformers to run the above scripts, e.g.:

      pip install torch==2.0.0
      pip install transformers==4.28.1
    
  • Run RedPajama chat model (q4_0):

      ./redpajama-chat -m ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Chat-3B-v1-q4_0.bin \
      -c 2048 \
      -b 128 \
      -n 1 \
      -t 8 \
      --instruct \
      --color \
      --top_k 30 \
      --top_p 0.95 \
      --temp 0.8 \
      --repeat_last_n 3 \
      --repeat_penalty 1.1 \
      --seed 0
    
  • Run other quantized version of RedPajama Chat model (Make sure you get the f16 model prepared before you run this):

    • Make the code to quantize the model if you have not:

      make quantize-gptneox
      
    • Generate the quantized model, the supported types include: q4_0, q4_1, q4_2, q5_0, q5_1, and q8_0. For example, to run q4_1, you need to do the following convertion:

      python ./examples/redpajama/scripts/quantize-gptneox.py ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Chat-3B-v1-f16.bin --quantize-output-type q4_1
      
    • Then you can chat with the quantized model:

      ./redpajama-chat -m ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Chat-3B-v1-q4_1.bin \
      -c 2048 \
      -b 128 \
      -n 1 \
      -t 8 \
      --instruct \
      --color \
      --top_k 30 \
      --top_p 0.95 \
      --temp 0.8 \
      --repeat_last_n 3 \
      --repeat_penalty 1.1 \
      --seed 0
      

RedPajama Base/Instruct model:

  • Make the code:

      make redpajama quantize-gptneox
    
  • Prepare the RedPajama Base/Instruct model (f16 and q4_0) for gglm:

      bash ./examples/redpajama/scripts/install-RedPajama-INCITE-Base-3B-v1.sh
    
      # Or 
    
      bash ./examples/redpajama/scripts/install-RedPajama-INCITE-Instruct-3B-v1.sh
    
  • Run other quantize version of RedPajama Base/Instruct model (Make sure you get the f16 model prepared before you run this). Then you can generate the quantized model, the supported types include: q4_0, q4_1, q4_2, q5_0, q5_1, and q8_0. For example, to run q4_1, you need to do the following convertion, e.g for RedPajama-Base q8_0:

      python ./examples/redpajama/scripts/quantize-gptneox.py ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Base-3B-v1-f16.bin --quantize-output-type q8_0
    
  • Run RedPajama Base/Instruct model (e.g., RedPajama-Instruct q8_0) :

      ./redpajama -m ./examples/redpajama/models/pythia/ggml-RedPajama-INCITE-Instruct-3B-v1-q8_0.bin \
      -c 2048 \
      -b 128 \
      -n 1 \
      -t 8 \
      --color \
      --top_k 30 \
      --top_p 0.95 \
      --temp 0.8 \
      --repeat_last_n 3 \
      --repeat_penalty 1.1 \
      --seed 0 \
      --n_predict 256 \
      --verbose-prompt \
      -p "How to schedule a tour to Anfield:"
    

Attribution

The following files are covered by a MIT license and were taken from:

https://github.com/byroneverson/gptneox.cpp

Thank you Byron.

common-gptneox.cpp	
copy-gptneox.cpp	
gptneox.cpp		
quantize-gptneox.cpp
common-gptneox.h	
gptneox-util.h		
gptneox.h
convert_gptneox_to_ggml.py
quantize-gptneox.py