ContextQMD
Back to Transformers

Cohere

docs/source/en/model_doc/cohere.md

5.8.05.8 KB
Original Source
<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. -->

This model was released on 2024-03-12 and added to Hugging Face Transformers on 2024-03-15.

<div style="float: right;"> <div class="flex flex-wrap space-x-1"> 
</div>
</div>

Cohere

Cohere Command-R is a 35B parameter multilingual large language model designed for long context tasks like retrieval-augmented generation (RAG) and calling external APIs and tools. The model is specifically trained for grounded generation and supports both single-step and multi-step tool use. It supports a context length of 128K tokens.

You can find all the original Command-R checkpoints under the Command Models 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], and from the command line.

<hfoptions id="usage"> <hfoption id="Pipeline">
python
from transformers import pipeline


pipeline = pipeline(
    task="text-generation",
    model="CohereForAI/c4ai-command-r-v01",
    device=0
)
pipeline("Plants create energy through a process known as")
</hfoption> <hfoption id="AutoModel">
python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", device_map="auto", attn_implementation="sdpa")

# format message with the Command-R chat template
messages = [{"role": "user", "content": "How do plants make energy?"}]
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 CohereForAI/c4ai-command-r-v01 --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 overview for more available quantization backends.

The example below uses bitsandbytes 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("CohereForAI/c4ai-command-r-v01")
model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", device_map="auto", quantization_config=bnb_config, attn_implementation="sdpa")

# format message with the Command-R chat template
messages = [{"role": "user", "content": "How do plants make energy?"}]
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))

Use the AttentionMaskVisualizer to better understand what tokens the model can and cannot attend to.

python
from transformers.utils.attention_visualizer import AttentionMaskVisualizer


visualizer = AttentionMaskVisualizer("CohereForAI/c4ai-command-r-v01")
visualizer("Plants create energy through a process known as")
<div class="flex justify-center"> </div>

Notes

  • Don't use the dtype parameter in [~AutoModel.from_pretrained] if you're using FlashAttention-2 because it only supports fp16 or bf16. You should use Automatic Mixed Precision, set fp16 or bf16 to True if using [Trainer], or use torch.autocast.

CohereConfig

[[autodoc]] CohereConfig

CohereTokenizer

[[autodoc]] CohereTokenizer

CohereModel

[[autodoc]] CohereModel - forward

CohereForCausalLM

[[autodoc]] CohereForCausalLM - forward