The provided modelling code, activated with trust_remote_code=True will automatically set the scale parameter from the configured max_position_embeddings. Eg for 8192, scale is set to 4.

python

from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, pipeline
import argparse

model_name_or_path = "TheBloke/Koala-7B-SuperHOT-8K-fp16"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
# Change this to the sequence length you want
config.max_position_embeddings = 8192

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
        config=config,
        trust_remote_code=True,
        device_map='auto')

# Note: check to confirm if this is correct prompt template is correct for this model!
prompt = "Tell me about AI"
prompt_template=f'''USER: {prompt}
ASSISTANT:'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

Using other UIs: monkey patch

Provided in the repo is llama_rope_scaled_monkey_patch.py, written by @kaiokendev.

It can be theoretically be added to any Python UI or custom code to enable the same result as trust_remote_code=True. I have not tested this, and it should be superseded by using trust_remote_code=True, but I include it for completeness and for interest.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

Patreon special mentions: zynix, ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.

Thank you to all my generous patrons and donaters!

Original model card: Kaio Ken's SuperHOT 8K

SuperHOT Prototype 2 w/ 8K Context

This is a second prototype of SuperHOT, a NSFW focused LoRA, this time 7B with 8K context and no RLHF, using the same technique described in the github blog.

Looking for Merged & Quantized Models?

Make some please :)

Using the monkey-patch?

You will NEED to apply the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192

The monkeypatch is only necessary if you are using a front-end/back-end that does not already support scaling and said front-end/back-end is Python-based (i.e. Huggingface Transformers). To apply the patch, you will need to copy the llama_rope_scaled_monkey_patch.py into your working directory and call the exported function replace_llama_rope_with_scaled_rope at the very start of your Python program. It will modify the Transformers library's implementation of RoPE to properly apply the scaling factor.

Using Oobabooga with Exllama?

Switch your loader to exllama or exllama_hf Add the arguments max_seq_len 8192 and compress_pos_emb 4. While the model may work well with compress_pos_emb 2, it was trained on 4, so that is what I advocate for you to use

Example in the command-line:

python server.py --max_seq_len 8192 --compress_pos_emb 4 --loader exllama_hf

In the UI, you will see the loader option in the Models tab. Once you select either exllama or exllama_hf, the max_seq_len and compress_pos_emb settings will appear.

Training Details

I trained the LoRA with the following configuration:

1200 samples (~400 samples over 2048 sequence length)
learning rate of 3e-4
3 epochs
The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
Rank = 4
Alpha = 8
no dropout
weight decay of 0.1
AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
Trained on 4-bit base model
Cutoff length: 4096

Original model card: Koala 7B

Chat & support: my new Discord server

Want to contribute? TheBloke's Patreon page

Koala: A Dialogue Model for Academic Research

This repo contains the weights of the Koala 7B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 7B model.

This version has then been converted to HF format.

My Koala repos

I have the following Koala model repositories available:

13B models:

7B models:

How the Koala delta weights were merged

The Koala delta weights were merged using the following commands:

git clone https://github.com/young-geng/EasyLM

git clone https://huggingface.co/nyanko7/LLaMA-7B

git clone https://huggingface.co/young-geng/koala koala_diffs

cd EasyLM

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_torch_to_easylm \
--checkpoint_dir=/content/LLaMA-7B \
--output_file=/content/llama-7B-LM \
--streaming=True

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.scripts.diff_checkpoint --recover_diff=True \
--load_base_checkpoint='params::/content/llama-7B-LM' \
--load_target_checkpoint='params::/content/koala_diffs/koala_7b_diff_v2' \
--output_file=/content/koala_7b.diff.weights \
--streaming=True

PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_easylm_to_hf --model_size=7b \
--output_dir=/content/koala-7B-HF \
--load_checkpoint='params::/content/koala_7b.diff.weights' \
--tokenizer_path=/content/LLaMA-7B/tokenizer.model

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Further info

Check out the following links to learn more about the Berkeley Koala model.

License

The model weights are intended for academic research only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.