初始化项目，由ModelHub XC社区提供模型
Model: Tesslate/Tessa-T1-7B Source: Original Platform
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README.md
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README.md
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---
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- qwen2
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- trl
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license: apache-2.0
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language:
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- en
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datasets:
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- Tesslate/Tessa-T1-Dataset
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---
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"Landing Page"
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## **Model Overview**
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Tessa-T1 is an innovative transformer-based **React reasoning model**, fine-tuned from the powerful **Qwen2.5-Coder-7B-Instruct** base model. Designed specifically for React frontend development, Tessa-T1 leverages advanced reasoning to autonomously generate well-structured, semantic React components. Its integration into agent systems makes it a powerful tool for automating web interface development and frontend code intelligence.
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---
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## **Model Highlights**
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- **React-specific Reasoning**: Accurately generates functional and semantic React components.
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- **Agent Integration**: Seamlessly fits into AI-driven coding agents and autonomous frontend systems.
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- **Context-Aware Generation**: Effectively understands and utilizes UI context to provide relevant code solutions.
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---
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## **Example Outputs**
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*See examples demonstrating the powerful reasoning and component creation capabilities of Tessa-T1:*
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AI upload
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Virtual Machine Console
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Playlist Management
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Prompt: "add in a calendar"
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---
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## **Use Cases**
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### **Recommended Uses**
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- **Automatic Component Generation**: Quickly produce React components from textual prompts.
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- **Agent-based Web Development**: Integrate into automated coding systems for faster frontend workflows.
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- **Frontend Refactoring**: Automate the optimization and semantic enhancement of React code.
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### **Limitations**
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- **Focused on React**: Limited use outside React.js frameworks.
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- **Complex State Management**: May require manual adjustments for highly dynamic state management scenarios.
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---
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## **How to Use**
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### **Inference Example**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "smirki/Tessa-T1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
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prompt = """<|im_start|>user
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Create a React component for a user profile card.<|im_end|>
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<|im_start|>assistant
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<|im_start|>think
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=1500, do_sample=True, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## **Performance and Evaluation**
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- **Strengths**:
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- Strong semantic React component generation.
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- Excellent integration capabilities with agent-based systems.
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- **Weaknesses**:
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- Complex JavaScript logic may require manual post-processing.
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---
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## **Technical Specifications**
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- **Architecture**: Transformer-based LLM
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- **Base Model**: Qwen2.5-Coder-7B-Instruct
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- **Precision**: bf16 mixed precision, quantized to q8
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- **Hardware Requirements**: Recommended 12GB VRAM
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- **Software Dependencies**:
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- Hugging Face Transformers
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- PyTorch
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---
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## **Citation**
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```bibtex
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@misc{smirki_Tessa-T1,
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title={Tessa-T1: React-Focused Reasoning Model for Component Generation},
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author={tesslate},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/tesslate/Tessa-T1}
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}
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```
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---
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## **Contact & Community**
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- **Creator:** [smirki](https://huggingface.co/tesslate)
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- **Repository & Demo**: Coming soon!
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-
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**Sponsored by vichar ai [Huggingface](https://huggingface.co/vicharai) [Website](https://vichar.io)**
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31
special_tokens_map.json
Normal file
31
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Normal file
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3
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217
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Normal file
217
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Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 120000,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"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