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Lilloukas' Platypus 30B fp16
This is fp16 pytorch format model files for Lilloukas' Platypus 30B merged with Kaio Ken's SuperHOT 8K.
Kaio Ken's SuperHOT 30b 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/Platypus-30B-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.
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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: Lilloukas' Platypus 30B
π₯³ Platypus-30B has arrived!
Platypus-30B is an instruction fine-tuned model based on the LLaMA-30B transformer architecture.
Metric | Value |
---|---|
MMLU (5-shot) | 64.2 |
ARC (25-shot) | 64.6 |
HellaSwag (10-shot) | 84.3 |
TruthfulQA (0-shot) | 45.8 |
Avg. | 64.7 |
We use state-of-the-art Language Model Evaluation Harness to run the benchmark tests above.
Model Details
- Trained by: Cole Hunter & Ariel Lee
- Model type: Platypus-30B is an auto-regressive language model based on the LLaMA transformer architecture.
- Language(s): English
- License for base weights: License for the base LLaMA model's weights is Meta's non-commercial bespoke license.
Hyperparameter | Value |
---|---|
33B | |
6656 | |
60 | |
52 |
Training Dataset
Dataset of highly filtered and curated question and answer pairs. Release TBD.
Training Procedure
lilloukas/Platypus-30B
was instruction fine-tuned using LoRA on 4 A100 80GB. For training details and inference instructions please see the Platypus-30B GitHub repo.
Reproducing Evaluation Results
Install LM Evaluation Harness:
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
Each task was evaluated on a single A100 80GB GPU.
ARC:
python main.py --model hf-causal-experimental --model_args pretrained=lilloukas/Platypus-30B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/arc_challenge_25shot.json --device cuda --num_fewshot 25
HellaSwag:
python main.py --model hf-causal-experimental --model_args pretrained=lilloukas/Platypus-30B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/hellaswag_10shot.json --device cuda --num_fewshot 10
MMLU:
python main.py --model hf-causal-experimental --model_args pretrained=lilloukas/Platypus-30B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/mmlu_5shot.json --device cuda --num_fewshot 5
TruthfulQA:
python main.py --model hf-causal-experimental --model_args pretrained=lilloukas/Platypus-30B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/truthfulqa_0shot.json --device cuda
Limitations and bias
The base LLaMA model is trained on various data, some of which may contain offensive, harmful, and biased content that can lead to toxic behavior. See Section 5.1 of the LLaMA paper. We have not performed any studies to determine how fine-tuning on the aforementioned datasets affect the model's behavior and toxicity. Do not treat chat responses from this model as a substitute for human judgment or as a source of truth. Please use responsibly.
Citations
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
@article{hu2021lora,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
journal={CoRR},
year={2021}
}
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