Model Card for C4AI Command R7B

Model Summary

C4AI Command R7B is an open weights research release of a 7B billion parameter model with advanced capabilities optimized for a variety of use cases including reasoning, summarization, question answering, and code. The model is trained to perform sophisticated tasks including Retrieval Augmented Generation (RAG) and tool use. The model also has powerful agentic capabilities with the ability to use and combine multiple tools over multiple steps to accomplish more difficult tasks. It obtains top performance on enterprise relevant code use cases. C4AI Command R7B is a multilingual model trained on 23 languages.

Developed by: Cohere and Cohere For AI

• Point of Contact: Cohere For AI: cohere.for.ai
  
• License: CC-BY-NC, requires also adhering to C4AI’s Acceptable Use Policy
  
• Model: c4ai-command-r7b-12-2024
  
• Model Size: 7 billion parameters
  
• Context length: 128K

Model Details

Input: Models input text only.

Output: Models generate text only.

Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. The model features three layers with sliding window attention (window size 4096) 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.

Languages covered: The model has been trained on 23 languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian.

Context length: Command R7B supports a context length of 128K.

A well-rounded model

Command R7B excels on standardized and externally verifiable benchmarks such as the HuggingFace Open LLM Leaderboard. Compared to other similarly sized open-weights models, Command R7B ranks first with strong performance across all tasks.

HuggingFace Leaderboard evaluation results. Competitor numbers are taken from the official leaderboard. Command R7B results are calculated by us using the official HuggingFace prompts and evaluation code.

Chat Capabilities:

Command R7B can be configured as both a conversational model and an instruct model. The conversational mode conditions the model on interactive behaviour, meaning it is expected to reply in a conversational fashion, provides introductory statements and follow-up questions, and uses Markdown as well as LaTeX where appropriate. It is optimized for interactive experiences, such as chatbots, where the model engages in dialogue.

The instruct mode, in contrast, conditions the model to provide concise yet comprehensive responses, and does not use Markdown / LaTeX by default. It is designed for non-interactive, task-focused use cases like extracting information, summarizing text, translation, and categorization.

Note: by default, Command R7B is delivered without a system preamble. We recommend to add the conversational or instruct preambles as described in our docs.

Code Capabilities:

Command R7B has meaningfully improved on code capabilities. In addition to academic code benchmarks, we have evaluated it on enterprise-relevant scenarios, including SQL and code translation, where it outperforms other models of similar size. Try these out by requesting code snippets, code explanations, or code rewrites. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.

From: https://huggingface.co/mmnga/c4ai-command-r7b-12-2024-gguf