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
- 3/12/2024 - We released Qwen2idae-16x14B-v1.0 on π€ HuggingFace, which has strong performance in Math and Code with 15B activated params.
- 2/7/2024 - Serp-ai adds unsloth support for faster and memory efficient training of our Parameter-Efficient Sparsity Crafting and releases new sparsetral models based on mistral-7B.
- 1/10/2024 - Camelidae models are now available on π€ 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 models (LLaMA 2, Yi, Qwen1.5, etc.).
| Camelidae Series | Download |
|---|---|
| Camelidae-8x7B | π€ HuggingFace |
| Camelidae-8x13B | π€ HuggingFace |
| Camelidae-8x34B | π€ HuggingFace |
| Camelidae-8x34B-pro | π€ Coming Soon |
| Qwen2idae Series | Download |
|---|---|
| Qwen2idae-16x14B-v1.0 | π€ HuggingFace |
| Qwen2idae-16x7B-v1.0 | π€ Coming Soon |
| Qwen2idae-16x1.8B-v1.0 | π€ Coming Soon |
| Model | Activated Params | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) |
|---|---|---|---|---|---|---|---|
| GPT3.5 | - | 70.0% | 57.1% | 34.1% | 48.1% | - | 85.5% |
| LLaMA2-70B-chat | 70B | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% |
| Camelidae-8x34B-pro | 35B | 75.7% | 79.4% | 24.0% | 48.8% | 43.2% | 85.2% |
| Camelidae-8x34B | 35B | 75.6% | 78.3% | 22.6% | 43.9% | 41.4% | 85.3% |
| SUSChat-34B | 34B | 76.4% | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% |
| Yi-34B-chat | 34B | 74.8% | 67.6% | 17.3% | 20.1% | 41.0% | 83.9% |
| Qwen2idae-16x14B-v1.0 | 15B | 66.7% | 77.8% | 29.9% | 62.8% | 48.6% | 82.3% |
| Mixtral-8x7B-instruct | 14B | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | 86.5% |
| Camelidae-8x13B | 13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% |
| LLaMA2-13B-chat | 13B | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% |
| Camelidae-8x7B | 7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% |
| LLaMA2-7B-chat | 7B | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% |
We bold the top3 scores separately for all models.
from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x34B", trust_remote_code=True) 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))
from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("hywu/Qwen2idae-16x14B-v1.0", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("hywu/Qwen2idae-16x14B-v1.0", device_map="auto", trust_remote_code=True).eval() inputs = tokenizer('<|im_start|>user\nHow are you?<|im_end|>\n<|im_start|>assistant\n', return_tensors='pt') inputs = inputs.to(model.device) pred = model.generate(**inputs) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
@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 He, Zhuolun 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 and Qwen2idae models are developed for academic research and free commercial use, all usage must adhere to the license from facebookresearch, 01-ai and Qwen1.5.