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WizardLM's WizardLM 13B V1.1 fp16

These are fp16 pytorch format model files for WizardLM's WizardLM 13B V1.1 merged with Kaio Ken's SuperHOT 8K .

Kaio Ken's SuperHOT 13b LoRA is merged on to the base model, and then 8K context can be achieved during inference by using trust_remote_code=True .

Note that config.json has been set to a sequence length of 8192. This can be modified to 4096 if you want to try with a smaller sequence length.

Repositories available

• 4-bit GPTQ models for GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference
• Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions
• Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions

How to use this model from Python code

First make sure you have Einops installed:

pip3 install auto-gptq

Then run the following code. config.json has been default to a sequence length of 8192, but you can also configure this in your Python code.

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 .

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

model_name_or_path = "TheBloke/WizardLM-13B-V1-1-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.

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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.

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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, 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: WizardLM's WizardLM 13B V1.1

This is the Full-Weight of WizardLM-13B V1.1 model.

Repository : https://github.com/nlpxucan/WizardLM

Twitter : https://twitter.com/WizardLM_AI/status/1677282955490918401

• ??? [7/7/2023] We released WizardLM V1.1 models. The WizardLM-13B-V1.1 is here ( Demo_13B-V1.1 , Demo_13B-V1.1_bak-1 , Demo_13B-V1.1_bak-2 , Full Model Weight ). WizardLM-7B-V1.1 , WizardLM-30B-V1.1 , and WizardLM-65B-V1.1 are coming soon. Please checkout the Full Model Weights and paper .
• ??? [7/7/2023] The WizardLM-13B-V1.1 achieves 6.74 on MT-Bench Leaderboard , 86.32% on AlpacaEval Leaderboard , and 99.3% on WizardLM Eval . (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)