Glacier R
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Glacier R+ 104B
Model Name: Glacier R+ 104B
Base Model: CohereForAI/c4ai-command-r-plus-08-2024
Finetuned by: Apache Labs
Model Description
Glacier R+ 104B is a specialized fine-tune of the CohereForAI/c4ai-command-r-plus-08-2024 model, created by Apache Labs to enhance performance in advanced language generation tasks. This model has been fine-tuned on domain-specific datasets, which improve its relevance, contextual accuracy, and fluency in conversation, creative content generation, and complex Q&A tasks.
Intended Use
Glacier R+ 104B is designed for the following use cases:
- Conversational AI: Enhanced for generating coherent, contextually accurate responses.
- Content Creation: Generates detailed, creative text based on input prompts.
- Question Answering: Provides reliable answers, leveraging strong contextual understanding.
- Summarization and Text Completion: Ideal for completing and summarizing complex texts with improved relevance and fluency.
Limitations and Considerations
- Context Length: Optimal for shorter to medium-length inputs (under 500 tokens).
- Bias and Fairness: Reflects the biases present in the training data. Use responsibly.
- Ethical Use: Avoid deployment in harmful, deceptive, or misleading applications.
How to Use
This model can be accessed through Hugging Face's transformers library. You can use the high-level pipeline or load the model and tokenizer directly.
Using a Pipeline
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="apache-labs/glacier-r-plus-104B")
response = pipe(messages)
print(response)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("apache-labs/glacier-r-plus-104B")
model = AutoModelForCausalLM.from_pretrained("apache-labs/glacier-r-plus-104B")
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Base model
CohereForAI/c4ai-command-r-plus-08-2024