huggingface--transformers/docs/source/en/model_doc/stablelm.md

This model was contributed to Hugging Face Transformers on 2024-02-14.

StableLM

Overview

StableLM 3B 4E1T (blog post) was proposed in StableLM 3B 4E1T: Technical Report 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

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

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed


set_seed(0)

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", device_map="auto")

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.

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 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:

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed


set_seed(0)

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", attn_implementation="flash_attention_2", device_map="auto")  # 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