Model Card: Nous-Yarn-Mistral-7b-64k
Model Description
Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method. It is an extension of Mistral-7B-v0.1 and supports a 64k token context window.
To use, pass trust_remote_code=True when loading the model, for example
model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k",
use_flash_attention_2=True,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True)
In addition you will need to use the latest version of transformers (until 4.35 comes out)
pip install git+https://github.com/huggingface/transformers
Benchmarks
Long context benchmarks:
| Model | Context Window | 8k PPL | 16k PPL | 32k PPL | 64k PPL | 128k PPL |
|---|---|---|---|---|---|---|
| Mistral-7B-v0.1 | 8k | 2.96 | - | - | - | - |
| Yarn-Mistral-7b-64k | 64k | 3.04 | 2.65 | 2.44 | 2.20 | - |
| Yarn-Mistral-7b-128k | 128k | 3.08 | 2.68 | 2.47 | 2.24 | 2.19 |
Short context benchmarks showing that quality degradation is minimal:
| Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
|---|---|---|---|---|---|
| Mistral-7B-v0.1 | 8k | 59.98 | 83.31 | 64.16 | 42.15 |
| Yarn-Mistral-7b-64k | 64k | 59.38 | 81.21 | 61.32 | 42.50 |
| Yarn-Mistral-7b-128k | 128k | 58.87 | 80.58 | 60.64 | 42.46 |
Collaborators
- bloc97: Methods, paper and evals
- @theemozilla: Methods, paper, model training, and evals
- @EnricoShippole: Model training
- honglu2875: Paper and evals
The authors would like to thank LAION AI for their support of compute for this model. It was trained on the JUWELS supercomputer.