Updated model card for OLMo2

docs/source/en/model_doc/olmo2.md

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+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
 # OLMo2
+[OLMo2](https://huggingface.co/papers/2501.00656) improves on [OLMo](./olmo) by changing the architecture and training recipes of the original models. This includes excluding all biases to improve training stability, non-parametric layer norm, SwiGLU activation function, rotary positional embeddings, and a modified BPE-based tokenizer that masks personal identifiable information. It is pretrained on [Dolma](https://huggingface.co/datasets/allenai/dolma), a dataset of 3T tokens.
 
-<div class="flex flex-wrap space-x-1">
-<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
-<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
-<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
-</div>
+You can find all the original OLMo2 checkpoints under the [OLMo2](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc) collection.
+
+> [!TIP]
+> Click on the OLMo2 models in the right sidebar for more examples of how to apply OLMo2 to different language tasks.
+
+The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`] and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    task="text-generation",
+    model="allenai/OLMo-2-0425-1B",
+    torch_dtype=torch.float16,
+    device=0,
+)
+
+result = pipe("Plants create energy through a process known as")
+print(result)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "allenai/OLMo-2-0425-1B"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "allenai/OLMo-2-0425-1B",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
+
+output = model.generate(**input_ids, max_length=50, cache_implementation="static")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model allenai/OLMo-2-0425-1B --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+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 [torchao](../quantization/torchao) to only quantize the weights to 4-bits.
+```py
+
+#pip install torchao
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
+
+torchao_config = TorchAoConfig(
+    "int4_weight_only",
+    group_size=128
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "allenai/OLMo-2-0425-1B"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "allenai/OLMo-2-0425-1B",
+    quantization_config=torchao_config,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
+
+output = model.generate(**input_ids, max_length=50, cache_implementation="static")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
 
-## Overview
+```
 
-The OLMo2 model is the successor of the OLMo model, which was proposed in
-[OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838).
 
- The architectural changes from the original OLMo model to this model are:
+## Notes
 
-- RMSNorm is used instead of standard layer norm.
-- Norm is applied to attention queries and keys.
-- Norm is applied after attention/feedforward layers rather than before.
+- OLMo2 uses RMSNorm instead of standard layer norm. The RMSNorm is applied to attention queries and keys, and it is applied after the attention and feedforward layers rather than before.
+- OLMo2 requires Transformers v4.48 or higher.
+- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`]. 
 
-This model was contributed by [shanearora](https://huggingface.co/shanearora).
-The original code can be found [here](https://github.com/allenai/OLMo/tree/main/olmo).
+    ```py
+    from transformers import AutoModelForCausalLM
+
+    model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")
+    ```
 
 
 ## Olmo2Config