The maximum number of tokens to generate. Shorter token lengths will provide faster performance.
A decimal number that determines the degree of randomness in the response
An alternative to sampling with temperature, where the model considers the results of the tokens with top_p probability mass.
The top-k parameter limits the model's predictions to the top k most probable tokens at each step of generation.
ResetGenerate
Output
Submit a prompt for a response.
Notes
Introduction
The Qwen1.5-32B model represents an exciting advancement in the Qwen1.5 language model series. The Qwen1.5-32B offers a significant reduction in memory usage and inference speed while still maintaining a high level of accuracy and capability. This model, featuring approximately 32 billion parameters, has been designed to be a "sweet spot" solution, particularly attractive for real-world applications where resource constraints are a consideration.
Qwen1.5-32B-Chat
The Qwen1.5-32B-Chat variant has been specifically enhanced for conversational AI, leveraging advancements in Reinforcement Learning from Human Feedback (RLHF) to offer improved dialogue interactions. The model achieves significant scores on established benchmarks such as MT-Bench and Alpaca-Eval 2.0, showing its effectiveness as a conversational agent.
Prompt Format
This format must be strictly respected for Chat Intruction Model variant, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
from clarifai.client.model import Model
prompt ='What’s the future of AI?'prompt_template = f"
<|im_start|>system
You are a helpful assistant<|im_end|><|im_start|>user
{prompt}<|im_end|><|im_start|>assistant
"
inference_params =dict(temperature=0.2, max_tokens=100, top_p=0.95, tok_k =40)# Model Predictmodel_prediction = Model("https://clarifai.com/qwen/qwenLM/models/qwen1_5-32B-chat").predict_by_bytes(prompt.encode(), input_type="text", inference_params=inference_params)print(model_prediction.outputs[0].data.text.raw)
You can also run Qwen1.5 API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.
Aliases: qwen, qwen1.5, Qwen1.5
Use Cases
Qwen1.5-32B and its chat-specific variant are well-suited for a variety of applications, including:
Customer service automation
Personal assistants
Multilingual translation services
Educational bots
Interactive entertainment systems.
Evaluation
The Qwen1.5-32B model has been rigorously evaluated across a variety of benchmarks to assess its performance in both general language understanding and specialized tasks. Below, we detail the performance of Qwen1.5-32B and compare it with other similar or larger models to provide a comprehensive overview of its capabilities.
General Language Understanding and Specialized Tasks
The following table showcases the performance of Qwen1.5-32B and other models across multiple benchmarks:
Model
MMLU (%)
C-Eval (%)
GSM8K (%)
MATH (%)
HumanEval (%)
MBPP (%)
BBH (%)
CMMLU (%)
Llama2-34B
62.6
-
42.2
6.2
22.6
33.0
44.1
-
Yi-34B
76.3
81.4
67.2
14.4
23.2
41.0
54.3
83.7
Mixtral-8x7B
70.6
-
74.4
28.4
40.2
60.7
-
-
Qwen1.5-72B
77.5
84.1
79.5
34.1
41.5
53.4
65.5
83.5
Qwen1.5-32B
73.4
83.5
77.4
36.1
37.2
49.4
66.8
82.3
Conversational Model Performance
The performance in conversational AI contexts has also been measured using MT-Bench and Alpaca-Eval 2.0. The results for these evaluations are detailed below:
Models
MT-Bench
AlpacaEval 2.0
Avg. Score
LC Win Rate (%)
Qwen1.5-72B-Chat
8.61
-
-
36.60
Qwen1.5-32B-Chat
8.30
-
-
27.49
Multilingual Capabilities
The Qwen1.5-32B model's performance across different languages is crucial for applications requiring multilingual support. Below are the results for a selection of language tasks:
Models
Exams (%)
Understanding (%)
Math (%)
Translation (%)
Average (%)
Mixtral-8x7B
56.08
70.70
45.00
29.78
50.39
Qwen1.5-72B
66.35
78.16
61.67
35.57
60.44
Qwen1.5-32B
61.57
76.48
56.13
33.46
56.91
Dataset
Qwen1.5-32B is trained on a diverse and expansive dataset compiled from various sources, including web text, books, and more specialized corpora covering scientific, technical, and multilingual content. This extensive training set ensures broad general knowledge and specialized understanding.
Advantages
Strong Performance: Rivals larger models in specific benchmark tasks, providing a practical alternative without significant trade-offs in output quality.
Versatility: Suitable for a broad range of applications from simple task automation to complex multilingual conversations.
Disclaimer
Please be advised that this model utilizes wrapped Artificial Intelligence (AI) provided by TogetherAI (the "Vendor"). These AI models may collect, process, and store data as part of their operations. By using our website and accessing these AI models, you hereby consent to the data practices of the Vendor. We do not have control over the data collection, processing, and storage practices of the Vendor. Therefore, we cannot be held responsible or liable for any data handling practices, data loss, or breaches that may occur. It is your responsibility to review the privacy policies and terms of service of the Vendor to understand their data practices. You can access the Vendor's privacy policy and terms of service at https://www.togetherai.com/legal/privacy-policy.
We disclaim all liability with respect to the actions or omissions of the Vendor, and we encourage you to exercise caution and to ensure that you are comfortable with these practices before utilizing the AI models hosted on our site.
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Text To Text
Input Type
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Description
Qwen1.5-32B model provides competitive AI performance with optimized efficiency, making it a strong choice for diverse, multilingual applications