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---
license: other
---
# xLSTM-7B
This xLSTM-7B was pre-trained on the DCLM and selected high-quality data for in a total of approx. 2.3 T tokens using the `xlstm-jax` framework.
## How to use it
First, install `xlstm`, which now uses the `mlstm_kernels` package for triton kernels:
```bash
pip install xlstm
pip install mlstm_kernels
```
For now, install the transformers repositiory fork from NX-AI (until it is merged):
```bash
pip install 'transformers @ git+ssh://git@github.com/NX-AI/transformers.git@integrate_xlstm'
```
Use this model as:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
xlstm = AutoModelForCausalLM.from_pretrained("NX-AI/xLSTM-7b", device_map="auto")
# this is a fork of EleutherAI/gpt-neox-20b
tokenizer = AutoTokenizer.from_pretrained("NX-AI/xLSTM-7b")
tokens = tokenizer("Hello xLSTM, how are you doing?", return_tensors='pt')['input_ids'].to(device="cuda")
out = xlstm.generate(tokens, max_new_tokens=20)
print(tokenizer.decode(out[0]))
```
## Speed results
Generation Speed using `torch.cuda.graph` and `torch.compile` optimizations on one NVIDIA H100:
## Performance
Using HuggingFace's `lm_eval`:
| BBH | MMLU-Pro | Math | MUSR | GPQA | IfEval |
|-------|----------|--------|------|------|--------|
| 0.381 | 0.242 | 0.036 | 0.379|0.280 | 0.244 |
Using HuggingFace's `lighteval` in the Leaderboard-v1 settings:
|Arc-Challenge (25-shot) |MMLU (5-shot) |Hellaswag (10-shot)|Winogrande (5-shot) |TruthfulQA (0-shot) |GSM8k (5-shot) |OpenbookQA (5-shot) | PiQA (5-shot)|
|------------------------|--------------|-------------------|--------------------|--------------------|---------------|--------------------|--------------|
| 0.584 |0.589 | 0.710 |0.742 | 0.420 | 0.004 | 0.443 | 0.817 |
## License
NXAI Community License (see `LICENSE` file)
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