stablelm.md

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# StableLM

## Overview

`StableLM 3B 4E1T` was proposed in [`StableLM 3B 4E1T`: Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Stability AI and is the first model in a series of multi-epoch pre-trained language models.

### Model Details

`StableLM 3B 4E1T` is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs.
The model architecture is transformer-based with partial Rotary Position Embeddings, SwiGLU activation, LayerNorm, etc.

We also provide `StableLM Zephyr 3B`, an instruction fine-tuned version of the model that can be used for chat-based applications.

### Usage Tips

- The architecture is similar to LLaMA but with RoPE applied to 25% of head embedding dimensions, LayerNorm instead of RMSNorm, and optional QKV bias terms.
- `StableLM 3B 4E1T`-based models uses the same tokenizer as [`GPTNeoXTokenizerFast`].

`StableLM 3B 4E1T` and `StableLM Zephyr 3B` can be found on the [Huggingface Hub](https://huggingface.co/stabilityai)

The following code snippet demonstrates how to use `StableLM 3B 4E1T` for inference:

```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> device = "cuda" # the device to load the model onto

>>> set_seed(0)

>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model.to(device)  # doctest: +IGNORE_RESULT

>>> model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)

>>> generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True)
>>> responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
>>> responses
['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
```

## Combining StableLM and Flash Attention 2

First, make sure to install the latest version of Flash Attention v2.

```bash
pip install -U flash-attn --no-build-isolation
```

Also make sure that your hardware is compatible with Flash-Attention 2. Read more about it in the official documentation of the [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Note: you must load your model in half-precision (e.g. `torch.bfloat16`).

Now, to run the model with Flash Attention 2, refer to the snippet below:

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> device = "cuda" # the device to load the model onto

>>> set_seed(0)

>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")  # doctest: +SKIP
>>> model.to(device)  # doctest: +SKIP

>>> model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)

>>> generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True)  # doctest: +SKIP
>>> responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)  # doctest: +SKIP
>>> responses  # doctest: +SKIP
['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
```


## StableLmConfig

[[autodoc]] StableLmConfig

## StableLmModel

[[autodoc]] StableLmModel
    - forward

## StableLmForCausalLM

[[autodoc]] StableLmForCausalLM
    - forward

## StableLmForSequenceClassification

[[autodoc]] StableLmForSequenceClassification
    - forward

## StableLmForTokenClassification

[[autodoc]] StableLmForTokenClassification
    - forward