初始化项目，由ModelHub XC社区提供模型
Model: prithivMLmods/Raptor-X1 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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tags:
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- text-generation-inference
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- Reasoning
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- Raptor
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- X1
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- Coder
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- Html
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- Css
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- React
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- Python
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- Java
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- Qwen
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datasets:
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- Tesslate/UIGEN-T1.5-Dataset
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- Tesslate/Tessa-T1-Dataset
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- smirki/UI_Reasoning_Dataset
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- reasoning-machines/gsm-hard
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language:
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- en
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base_model:
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- prithivMLmods/Viper-Coder-v1.4
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pipeline_tag: text-generation
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library_name: transformers
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---
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# **Raptor X1**
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> [!warning]
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> Raptor X1 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced coding reasoning and UI coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor X1 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
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Key improvements include:
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1. **Enhanced Coding Reasoning**: Provides in-depth explanations and optimizations for complex coding problems, making it useful for developers and engineers.
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2. **Advanced UI Coding Support**: Excels in generating and refining front-end code for web and mobile applications.
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3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation.
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4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
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5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
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> [!WARNING]
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>Prompt Style :
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>
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> Make a dark-themed minimalist dashboard for an **oil rig**.
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>
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> [HTML, CSS, and more if required].
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# **Quickstart with transformers**
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Raptor-X1"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "How do I optimize React performance?"
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messages = [
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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# **Intended Use**
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1. **Coding Reasoning**:
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Designed for providing explanations, optimizations, and best practices for coding problems.
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2. **UI Coding and Development**:
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Excels in front-end development, including React, Vue, and other UI frameworks.
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3. **Programming and Software Development**:
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Capable of generating, analyzing, and optimizing code in multiple programming languages.
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4. **Educational Assistance**:
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Helps developers by providing coding tutorials, debugging assistance, and structured learning material.
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5. **Multilingual Applications**:
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Supports global communication, translations, and multilingual content generation.
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6. **Long-Form Content Generation**:
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Can generate extended responses, including documentation, technical reports, and coding guides.
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# **Limitations**
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1. **Hardware Requirements**:
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Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.
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2. **Potential Bias in Responses**:
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While designed to be neutral, outputs may still reflect biases present in training data.
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3. **Complexity in Some Advanced Topics**:
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While proficient in general coding, highly specialized fields may require verification.
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4. **Limited Real-World Awareness**:
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Does not have access to real-time events beyond its training cutoff.
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5. **Error Propagation in Extended Outputs**:
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Minor errors in early responses may affect overall coherence in long-form outputs.
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6. **Prompt Sensitivity**:
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The effectiveness of responses may depend on how well the input prompt is structured.
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