-
+
-## Resources
+## GPT-Neo
-- [Text classification task guide](../tasks/sequence_classification)
-- [Causal language modeling task guide](../tasks/language_modeling)
+[GPT-Neo](https://zenodo.org/records/5297715) is an open-source alternative to GPT-2 and GPT-3 models, built with Mesh TensorFlow for TPUs. GPT-Neo uses local attention in every other layer for more efficiency. It is trained on the [Pile](https://huggingface.co/datasets/EleutherAI/pile), a diverse dataset consisting of 22 smaller high-quality datasets.
+
+
+You can find all the original GPT-Neo checkpoints under the [EleutherAI](https://huggingface.co/EleutherAI?search_models=gpt-neo) organization.
+
+> [!TIP]
+> Click on the GPT-Neo models in the right sidebar for more examples of how to apply GPT Neo to different language tasks.
+
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`], and from the command line.
+
+
+
+
+```py
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(task="text-generation", model="EleutherAI/gpt-neo-1.3B", torch_dtype=torch.float16, device=0)
+pipeline("Hello, I'm a language model")
+```
+
+
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B", torch_dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2")
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
+
+input_ids = tokenizer("Hello, I'm a language model", return_tensors="pt").to("cuda")
+
+output = model.generate(**input_ids)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+
+
+
+```bash
+echo -e "Hello, I'm a language model" | transformers-cli run --task text-generation --model EleutherAI/gpt-neo-1.3B --device 0
+```
+
+
+
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_compute_dtype="float16",
+ bnb_4bit_use_double_quant=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+ "EleutherAI/gpt-neo-2.7B",
+ quantization_config=quantization_config,
+ device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
+inputs = tokenizer("Hello, I'm a language model", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Notes
+
+- Pad inputs on the right because GPT-Neo uses absolute position embeddings.
## GPTNeoConfig
-
-
-
+