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+*This model was contributed to Hugging Face Transformers on 2024-12-13.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <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">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Cohere 2
+
+[Cohere Command R7B](https://cohere.com/blog/command-r7b) is an open weights research release of a 7B billion parameter model. It is a multilingual model trained on 23 languages and has a context window of 128k. The model features three layers with sliding window attention and ROPE for efficient local context modeling and relative positional encoding. A fourth layer uses global attention without positional embeddings, enabling unrestricted token interactions across the entire sequence.
+
+This model is optimized for speed, cost-performance, and compute resources.
+
+You can find all the original Command-R checkpoints under the [Command Models](https://huggingface.co/collections/CohereForAI/command-models-67652b401665205e17b192ad) collection.
+
+> [!TIP]
+> Click on the Cohere models in the right sidebar for more examples of how to apply Cohere to different language tasks.
+
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class, and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="text-generation",
+    model="CohereLabs/c4ai-command-r7b-12-2024",
+    device_map=0
+)
+
+messages = [
+    {"role": "user", "content": "Hello, can you please help me book a hotel in Japan?"},
+]
+pipeline(messages)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("CohereLabs/c4ai-command-r7b-12-2024")
+model = AutoModelForCausalLM.from_pretrained(
+    "CohereLabs/c4ai-command-r7b-12-2024",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+# format message with the Command-R chat template
+messages = [{"role": "user", "content": "Hello, can you please help me book a hotel in Japan?"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+output = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.3,
+    cache_implementation="static",
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+# pip install -U flash-attn --no-build-isolation
+transformers chat CohereLabs/c4ai-command-r7b-12-2024 --dtype auto --attn_implementation flash_attention_2
+```
+
+</hfoption>
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview.md) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes.md) to quantize the weights to 4-bits.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+
+bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+tokenizer = AutoTokenizer.from_pretrained("CohereLabs/c4ai-command-r7b-12-2024")
+model = AutoModelForCausalLM.from_pretrained(
+    "CohereLabs/c4ai-command-r7b-12-2024",
+    device_map="auto",
+    quantization_config=bnb_config,
+    attn_implementation="sdpa"
+)
+
+# format message with the Command-R chat template
+messages = [{"role": "user", "content": "Hello, can you please help me book a hotel in Japan?"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+output = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.3,
+    cache_implementation="static",
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Cohere2Config
+
+[[autodoc]] Cohere2Config
+
+## Cohere2Model
+
+[[autodoc]] Cohere2Model
+    - forward
+
+## Cohere2ForCausalLM
+
+[[autodoc]] Cohere2ForCausalLM
+    - forward