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/llama-30b-supercot-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 I have not tested this, and it should be superseded by using trust_remote_code=True, but I include it for completeness and for interest.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

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Thanks, and how to contribute.

Thanks to the chirper.ai team!

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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.

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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, this time 30B with 8K context and no RLHF, using the same technique described in the github blog. Tests have shown that the model does indeed leverage the extended context at 8K.

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

Looking for Merged & Quantized Models?

30B 4-bit CUDA: tmpupload/superhot-30b-8k-4bit-safetensors
30B 4-bit CUDA 128g: tmpupload/superhot-30b-8k-4bit-128g-safetensors

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

Original model card: Ausboss' Llama 30B SuperCOT

Merge of huggyllama/llama-30b + kaiokendev/SuperCOT-LoRA

Supercot was trained to work with langchain prompting.

Load up locally in my custom LLM notebook that uses the Oobabooga modules to load up models: https://github.com/ausboss/Local-LLM-Langchain

Then you can add cells from of these other notebooks for testing: https://github.com/gkamradt/langchain-tutorials

From Koikendev Lora page

Compatibility

This LoRA is compatible with any 7B, 13B or 30B 4-bit quantized LLaMa model, including ggml quantized converted bins

Prompting

You should prompt the LoRA the same way you would prompt Alpaca or Alpacino:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
<instruction>

### Input:
<any additional context. Remove this if it's not neccesary>

### Response:
<make sure to leave a single new-line here for optimal results>

Remember that with lower parameter sizes, the structure of the prompt becomes more important. The same prompt worded differently can give wildly different answers. Consider using the following suggestion suffixes to improve output quality:

"Think through this step by step"
"Let's think about this logically"
"Explain your reasoning"
"Provide details to support your answer"
"Compare and contrast your answer with alternatives"

Coming Soon

Tweet fix for 13B and 7B - lower model sizes seem to be extremely sensitive to hashtags at the end of training data responses, especially at longer cutoffs