This fork introduces mistral support (aka sparsetral)
- (2/4/2024) Add unsloth support for faster and memory efficient training (forked repo with sparsetral support)
- (1/29/2024) v1 sparsetral is on huggingface!
- (1/28/2024) Add vLLM support (forked repo with sparsetral support)
- (1/28/2024) Sparsetral (16 experts, top_k 4) trained with slim-orca for 1 epoch with an effective batch size of 32 at 4096 seq_len is currently finishing training and coming soon!
Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
- 1/10/2024 - Camelidae models are now available on 🤗HuggingFace.
- 1/4/2024 - We released the paper, Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks.
- 12/22/2023 - We released the training repo that craft the dense model with LLaMA architecture to the MoE model.
We present Parameter-Efficient Sparsity Crafting to help dense models learn knowledge from different fields (including code and math). This appraoch perfrom instruction tuning and utilize MoE structure in an efficient way.
Parameter-Efficient Sparsity Crafting utilizes parameter efficient techiniques including QLoRA and Adapter to perfrom Efficient Sparse Upcycling.
The repo supports the training of dense model using LLaMA architecture (LLaMA 2, Yi, etc.)
| Model | Download |
|---|---|
| Camelidae-8x7B | 🤗HuggingFace |
| Camelidae-8x13B | 🤗HuggingFace |
| Camelidae-8x34B | 🤗HuggingFace |
| Model | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) | TriviaQA (0shot) |
|---|---|---|---|---|---|---|---|
| GPT3.5 | 70.0% | 57.1% | 34.1% | 48.1% | - | 85.5% | - |
| Camelidae-8x34B | 75.6% | 78.3% | 22.6% | 43.9% | 41.4% | 85.3% | 63.4% |
| SUSChat-34B | 76.4% | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% | 56.1% |
| Mixtral-8x7B-instruct | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | 86.5% | 57.7% |
| LLaMA2-70B-chat | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% | 63.0% |
| Camelidae-8x13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% | 59.4% |
| LLaMA2-13B-chat | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% | 55.0% |
| Camelidae-8x7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% | 51.0% |
| LLaMA2-7B-chat | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% | 46.4% |
We bold the highest scores for open-source models and all models separately.
from transformers import AutoModelForCausalLM, AutoTokenizer
# tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x7B", trust_remote_code=True)
# tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x13B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x34B", trust_remote_code=True)
# model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x7B", device_map="auto", trust_remote_code=True).eval()
# model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x13B", device_map="auto", trust_remote_code=True).eval()
model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x34B", device_map="auto", trust_remote_code=True).eval()
inputs = tokenizer('### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# I am doing well, thank you.@article{wu2024parameter,
title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
journal={arXiv preprint arXiv:2401.02731},
year={2024}
}The source code in this repo is licensed under the Apache 2.0 License. Camelidae models are developed for academic research and free commercial use, all usage must adhere to the license from facebookresearch and 01-ai.
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