Deepseek 0628 Gguf

Table of Contents

Model Overview

The DeepSeek-V2-Chat-0628 model is a highly advanced AI chatbot that has achieved impressive rankings in the LMSYS Chatbot Arena Leaderboard.

Key Attributes

• Rankings:
  • Overall Arena Ranking: #11 global
  • Coding Arena Ranking: #3 global
  • Hard Prompts Arena Ranking: #7 global
• Model Size:
  • 132.1 GiB (IQ4XM version)
  • 440 GiB (BF16 version)
• Quantizations:
  • IQ4XM (4-bit)
  • Q8_0 (8-bit)
  • BF16 (16-bit)
• Performance:
  • Perplexity: 5.8620 +/- 0.26853 (IQ4XM version)
  • Perplexity: 5.8782 +/- 0.27021 (Q8_0 version)
  • Perplexity: 5.8734 +/- 0.26967 (BF16 version)

Tasks and rank

Text Generation 94th

Response Generation 43rd

Natural Language Understanding 76th

Language Modelling 87th

Chatbot 64th

Learn More About the Ranking System

Capabilities

The DeepSeek-V2-Chat-0628 model is capable of generating human-like text and can be used for a variety of tasks, including:

• Conversational dialogue
• Code generation
• Answering complex questions

One of the unique features of this model is its ability to perform well in hard prompts, making it a great choice for applications that require a high level of accuracy and coherence.

Performance Benchmarks

Model	Perplexity	Model Size
DeepSeek-V2-Chat-0628 (IQ4XM)	5.8620 +/- 0.26853	132.1 GiB
==Claude Opus==	5.90 +/- 0.28	250 GiB
==DeepSeek-V2-Chat-0628 (BF16)==	5.8734 +/- 0.26967	440 GiB

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Usage

To use the DeepSeek-V2-Chat-0628 model, you can download the IQ4XM version, which is optimized for CPU performance. You can also use the Q8_0 or BF16 versions, which offer higher accuracy but require more computational resources.

Examples

Here’s an example of how to use the DeepSeek-V2-Chat-0628 model in a command-line interface:

./llama-cli -m ~/r/deepseek_0628_cpu_optimized_iq4xm-00001-of-00004.gguf -t 62 --temp 0.4 -co -cnv -i -c 3000 -p "Adopt the persona of a full-stack developer at NASA JPL."

This code snippet shows how to use the llama-cli tool to interact with the DeepSeek-V2-Chat-0628 model, specifying the model file, temperature, and input prompt.

Examples

Write a Python function to add two numbers. def add(a, b): return a + b

Explain the concept of quantization in deep learning models. Quantization in deep learning models refers to the process of reducing the precision of the model's weights and activations from floating-point numbers to integers, typically 8-bit or 16-bit integers. This reduction in precision can lead to significant improvements in model efficiency, including reduced memory usage and faster inference times, while maintaining acceptable accuracy.

How does the DeepSeek-V2-Chat-0628 model compare to other models in terms of performance? According to the LMSYS Chatbot Arena Leaderboard, the DeepSeek-V2-Chat-0628 model ranks #7 globally in the Hard Prompts Arena, outperforming other models like Claude Opus even in English-only hard-prompts.

Limitations

The DeepSeek-V2-Chat-0628 model is not perfect and has its limitations.

Model Size and Performance

• The model’s size can be a challenge. The DeepSeek-V2-Chat-0628 is approximately 132.1 GiB in size, which can make it difficult to download and store.
• The model’s performance can also be affected by the hardware it’s running on. For example, the model’s performance may be slower on older CPUs.

Quantization and Bit Depth

• The model uses quantization to reduce its size, but this can also affect its performance. The 4-bit IQ4XM version, for example, may not be as accurate as the full 16-bit BF16 version.
• The model’s bit depth can also impact its performance. The 1-bit IQ1M and 1-bit IQ1S versions, for example, may not be as accurate as the 4-bit IQ4XM version.

Perplexity and Coherence

• The model’s perplexity can be a challenge. The model’s perplexity is a measure of how well it can understand and respond to complex prompts. The DeepSeek-V2-Chat-0628 has a perplexity of 5.8620 +/- 0.26853, which is relatively high.
• The model’s coherence can also be a challenge. The model’s coherence is a measure of how well it can generate responses that are consistent and logical. The DeepSeek-V2-Chat-0628 may struggle with coherence in certain scenarios.

Comparison to Other Models

• The DeepSeek-V2-Chat-0628 is not the only AI model available. ==Other Models==, such as the Claude Opus model, may have different strengths and weaknesses.
• The DeepSeek-V2-Chat-0628 may not always perform better than other models. For example, the Claude Opus model may perform better in certain scenarios.

Format

The DeepSeek-V2-Chat-0628 model uses a transformer architecture and accepts input in the form of text sequences. To get the most out of this model, you’ll need to understand its format and requirements.

Architecture

The DeepSeek-V2-Chat-0628 is built on a transformer architecture, which is a type of neural network designed specifically for natural language processing tasks. This architecture allows the model to handle long-range dependencies in text data and generate coherent responses.

Data Formats

This model supports various data formats, including:

• Text sequences: The model accepts input in the form of text sequences, which can be tokenized and pre-processed for optimal performance.
• GGML TYPE IQ_4_XS 4bit: The model uses a custom quantization format, IQ_4_XS 4bit, which provides a balance between performance and memory usage.

Input Requirements

To use the DeepSeek-V2-Chat-0628 model, you’ll need to provide input in the following format:

• Text sequence: A single text sequence, which can be a sentence, paragraph, or longer piece of text.
• Tokenization: The input text sequence should be tokenized, which involves breaking the text into individual words or subwords.
• Pre-processing: The tokenized text sequence should be pre-processed to optimize performance.

Output Requirements

The model generates output in the form of text sequences, which can be used for a variety of tasks, such as:

• Text generation: The model can generate coherent text responses to a given prompt or input sequence.
• Conversational dialogue: The model can be used to generate conversational dialogue, responding to user input in a natural and engaging way.