qwen2-7b-instruct
Maintainer: zsxkib
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| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The qwen2-7b-instruct is a 7 billion parameter language model from Alibaba Cloud, fine-tuned for chat completions. It is part of the Qwen2 series of large language models, which have demonstrated performance competitive with state-of-the-art open-source models across a range of benchmarks.
Compared to the previous Qwen1.5 models, Qwen2 has generally surpassed most open-source models and shown competitiveness against proprietary models in language understanding, generation, multilingual capability, coding, mathematics, and reasoning tasks. The Qwen2-7B-Instruct model in particular supports context lengths up to 131,072 tokens, enabling processing of extensive inputs.
Model inputs and outputs
Inputs
- seed: The seed for the random number generator
- top_k: When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens
- top_p: When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens
- prompt: Input prompt
- model_type: Choose from available 7B Qwen2 models
- temperature: Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic
- system_prompt: System prompt
- max_new_tokens: The maximum number of tokens to generate
- repetition_penalty: Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it
Outputs
- The model generates a sequence of tokens in response to the input prompt and context.
Capabilities
The qwen2-7b-instruct model has demonstrated strong performance on a variety of benchmarks, including English tasks like MMLU, coding tasks like HumanEval, and mathematics tasks like GSM8K. It has also shown impressive capabilities on Chinese evaluation datasets like C-Eval.
Compared to similar-sized instruction-tuned models like meta-llama-3-70b-instruct, the Qwen2-7B-Instruct model generally outperforms on these tasks. For example, it achieves 70.5% on MMLU versus 68.4% for LLaMA-3-8B-Instruct, and 79.9% on HumanEval versus 62.2% for LLaMA-3-8B-Instruct.
The model also supports long-context processing using techniques like NTK-aware interpolation and LogN attention scaling, which allow it to maintain strong performance on long text summarization datasets like VCSUM.
What can I use it for?
The qwen2-7b-instruct model can be used for a variety of natural language processing tasks, including open-ended chat, question answering, text generation, and code generation. Given its strong performance on benchmarks, it could be a good choice for applications that require language understanding, reasoning, and generation capabilities.
For example, you could use the model to build a virtual assistant that can engage in open-ended conversations, answer questions, and even assist with coding tasks. The long-context processing capabilities could also make it useful for summarization or analysis of lengthy documents.
Things to try
One interesting aspect of the Qwen2 models is their support for a large vocabulary of over 150,000 tokens. This makes them more friendly to multilingual usage, as users can enhance capabilities for specific languages without needing to expand the vocabulary.
You could experiment with using the qwen2-7b-instruct model for tasks in languages beyond English and Chinese, and see how it performs compared to models with smaller, more language-specific vocabularies. The ability to handle a diverse range of languages could be a valuable asset for certain applications.
Another area to explore is the model's capabilities around tool usage and task-oriented prompting, as demonstrated by its strong performance on the ReAct Prompting and HuggingFace Agent benchmarks. You could try integrating the model with external tools or APIs and see how it handles complex, multi-step workflows.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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