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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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license: apache-2.0
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language:
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- en
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- de
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- ar
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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<div align="center">
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<img src="https://i.ibb.co/CBHmTDn/136719a5-6d8a-4654-a618-46eabc788953.jpg" alt="Arcee-Agent" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
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</div>
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Arcee Agent is a cutting-edge 7B parameter language model specifically designed for function calling and tool use. Initialized from Qwen2-7B, it rivals the performance of much larger models while maintaining efficiency and speed. This model is particularly suited for developers, researchers, and businesses looking to implement sophisticated AI-driven solutions without the computational overhead of larger language models. Compute for training Arcee-Agent was provided by [CrusoeAI](https://huggingface.co/crusoeai). Arcee-Agent was trained using [Spectrum](https://arxiv.org/abs/2406.06623).
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GGUFs are available from [CrusoeAI](https://huggingface.co/crusoeai/Arcee-Agent-GGUF).
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### Key Features
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1. **Advanced Function Calling:** Arcee Agent excels at interpreting, executing, and chaining function calls. This capability allows it to interact seamlessly with a wide range of external tools, APIs, and services.
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2. **Multiple Format Support:** The model is compatible with various tool use formats, including:
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- Glaive FC v2
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- Salesforce
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- Agent-FLAN
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Arcee-Agent performs best when using the VLLM OpenAI FC format, but it also excels with prompt-based solutions. Agent-Spark can accommodate any specific use case or infrastructure needs you may have.
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4. **Dual-Mode Functionality:**
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- Tool Router: Arcee Agent can serve as intelligent middleware, analyzing requests and efficiently routing them to appropriate tools or larger language models for processing.
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- Standalone Chat Agent: Despite its focus on function calling, Arcee Agent is capable of engaging in human-like conversations and completing a wide range of tasks independently.
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5. **Unparalleled Speed and Efficiency:** With its 7B parameter architecture, Arcee Agent delivers rapid response times and efficient processing, making it suitable for real-time applications and resource-constrained environments.
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6. **Competitive Performance:** In function calling and tool use tasks, Arcee Agent competes with the capabilities of models many times its size, offering a cost-effective solution for businesses and developers.
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## Detailed Function Calling and Tool Use Capabilities
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Arcee Agent's function calling and tool use capabilities open up a world of possibilities for AI-driven applications. Here's a deeper look at what you can achieve:
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1. **API Integration:** Seamlessly interact with external APIs, allowing your applications to:
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- Fetch real-time data (e.g., stock prices, weather information)
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- Post updates to social media platforms
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- Send emails or SMS messages
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- Interact with IoT devices
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2. **Database Operations:** Execute complex database queries and operations through natural language commands, enabling:
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- Data retrieval and analysis
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- Record updates and insertions
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- Schema modifications
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3. **Code Generation and Execution:** Generate and run code snippets in various programming languages, facilitating:
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- Quick prototyping
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- Automated code review
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- Dynamic script generation for data processing
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4. **Multi-step Task Execution:** Chain multiple functions together to complete complex tasks, such as:
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- Booking travel arrangements (flights, hotels, car rentals)
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- Generating comprehensive reports from multiple data sources
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- Automating multi-stage business processes
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## Business Use Cases
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Arcee Agent's unique capabilities make it an invaluable asset for businesses across various industries. Here are some specific use cases:
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1. **Customer Support Automation:**
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- Implement AI-driven chatbots that handle complex customer inquiries and support tickets.
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- Automate routine support tasks such as password resets, order tracking, and FAQ responses.
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- Integrate with CRM systems to provide personalized customer interactions based on user history.
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2. **Sales and Marketing Automation:**
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- Automate lead qualification and follow-up using personalized outreach based on user behavior.
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- Generate dynamic marketing content tailored to specific audiences and platforms.
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- Analyze customer feedback from various sources to inform marketing strategies.
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3. **Operational Efficiency:**
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- Automate administrative tasks such as scheduling, data entry, and report generation.
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- Implement intelligent assistants for real-time data retrieval and analysis from internal databases.
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- Streamline project management with automated task assignment and progress tracking.
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4. **Financial Services Automation:**
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- Automate financial reporting and compliance checks.
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- Implement AI-driven financial advisors for personalized investment recommendations.
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- Integrate with financial APIs to provide real-time market analysis and alerts.
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5. **Healthcare Solutions:**
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- Automate patient record management and data retrieval for healthcare providers.
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6. **E-commerce Enhancements:**
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- Create intelligent product recommendation systems based on user preferences and behavior.
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- Automate inventory management and supply chain logistics.
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- Implement AI-driven pricing strategies and promotional campaigns.
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7. **Human Resources Automation:**
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- Automate candidate screening and ranking based on resume analysis and job requirements.
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- Implement virtual onboarding assistants to guide new employees through the onboarding process.
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- Analyze employee feedback and sentiment to inform HR policies and practices.
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8. **Legal Services Automation:**
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- Automate contract analysis and extraction of key legal terms and conditions.
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- Implement AI-driven tools for legal research and case law summarization.
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- Develop virtual legal assistants to provide preliminary legal advice and document drafting.
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9. **Educational Tools:**
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- Create personalized learning plans and content recommendations for students.
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- Automate grading and feedback for assignments and assessments.
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10. **Manufacturing and Supply Chain Automation:**
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- Optimize production schedules and inventory levels using real-time data analysis.
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- Implement predictive maintenance for machinery and equipment.
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- Automate quality control processes through data-driven insights.
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## Benchmarking
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<div align="center">
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<img src="https://i.ibb.co/xmgswP8/Screenshot-2024-07-02-at-1-49-04-PM.png" alt="Arcee-Agent-Evals" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
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</div>
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## Intended Uses
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Arcee Agent is designed for a wide range of applications where efficient function calling and tool use are crucial. Some potential use cases include:
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||||
- Developing sophisticated chatbots and virtual assistants with advanced tool integration
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- Creating efficient middleware for routing and preprocessing requests to larger language models
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- Implementing AI-driven process automation in resource-constrained environments
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- Prototyping and testing complex tool-use scenarios without the need for more computationally expensive models
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- Building interactive documentation systems that can execute code examples in real-time
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- Developing intelligent agents for IoT device management and home automation
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- Creating AI-powered research assistants for various scientific disciplines
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## Limitations
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While Arcee Agent excels in its specialized areas, users should be aware of its limitations:
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- The model's general knowledge and capabilities outside of function calling and tool use may be more limited compared to larger, general-purpose language models.
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- Performance in tasks unrelated to its core functionalities may not match that of models with more diverse training.
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- As with all language models, outputs should be validated and used responsibly, especially in critical applications.
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- The model's knowledge cutoff date may limit its awareness of recent events or technological advancements.
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## Usage
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The model was trained to respect many different formats - but the evals were done with this specific tool template:
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('QwenCollection/Arcee-Agent')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/QwenCollection/Arcee-Agent.git
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```
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In this environment, you have access to a set of tools you can use to answer the user's question.
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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You may call them like this:
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<function_calls>
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<invoke>
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<tool_name>$TOOL_NAME</tool_name>
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<parameters>
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<$PARAMETER_NAME>$PARAMETER_VALUE</$PARAMETER_NAME>
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...
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</parameters>
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</invoke>
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</function_calls>
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Here are the tools available:
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<tools>
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```
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{
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"_name_or_path": "/workspace/models/agentic-spark",
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"architectures": [
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"Qwen2ForCausalLM"
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 131072,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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{
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"_from_model_config": true,
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"do_sample": true,
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"transformers_version": "4.42.3",
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"use_cache": false
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||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
303112
tokenizer.json
Normal file
303112
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user