初始化项目，由ModelHub XC社区提供模型
Model: vanta-research/atom-olmo3-7b Source: Original Platform
This commit is contained in:
ModelHub XC
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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245
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- allenai/Olmo-3-7B-Instruct
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base_model_relation: finetune
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library_name: transformers
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tags:
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- conversational-ai
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- cognitive-architectures
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- chat
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- chatbot
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- safetensors
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- persona
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- text
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- text-generation
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- persona-ai
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- roleplay
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- cognitive
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- vanta-research
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- project-atom
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- atom
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- conversational
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- collaborative-ai
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- text-generation-inference
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- collaboration
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- friendly
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- educational
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- learning
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- ai-research
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- ai-alignment-research
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- ai-alignment
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- ai-behavior-research
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- ai-persona-research
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- human-ai-collaboration
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---
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<div align="center">
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<h1>VANTA Research</h1>
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<p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
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<p>
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<a href="https://vantaresearch.xyz"><img src="https://img.shields.io/badge/Website-vantaresearch.xyz-black" alt="Website"/></a>
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<a href="https://merch.vantaresearch.xyz"><img src="https://img.shields.io/badge/Merch-merch.vantaresearch.xyz-sage" alt="Merch"/></a>
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<a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
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<a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
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</p>
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</div>
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---
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# Atom-Olmo3-7B
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Atom-Olmo3-7B is a specialized language model fine-tuned for collaborative problem-solving and creative exploration. Built on the Olmo-3-7B-Instruct foundation, this model brings thoughtful, structured analysis to complex questions while maintaining an engaging, conversational tone.
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## Key Features
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- **Apache 2.0 License**: Fully open-source with permissive licensing for commercial use
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- **Collaborative Intelligence**: Trained to ask clarifying questions and explore ideas iteratively
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- **Structured Thinking**: Provides organized, framework-driven responses for complex topics
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- **Educational Depth**: Breaks down sophisticated concepts into accessible explanations
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- **Creative Synthesis**: Combines analytical rigor with imaginative problem-solving
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## Model Details
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- **Base Model**: allenai/Olmo-3-7B-Instruct
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- **Training Method**: LoRA fine-tuning (r=16, alpha=32)
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- **Training Data**: Curated dataset focused on collaborative reasoning, ELI5 explanations, lateral thinking, and research synthesis
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- **Context Length**: 4096 tokens (recommended)
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- **Parameters**: 7B
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- **Precision**: FP16
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## Intended Use
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### Primary Use Cases
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- Technical brainstorming and ideation
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- Educational explanations and concept breakdowns
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- Research synthesis and literature review
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- Collaborative problem-solving across domains
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- Framework development and structured analysis
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### Out of Scope
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This model is not intended for:
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- Medical diagnosis or treatment recommendations
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- Legal advice or financial counseling
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- Real-time factual information (knowledge cutoff applies)
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- Autonomous decision-making in high-stakes scenarios
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## Training Details
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### Dataset
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The model was trained on a specialized dataset comprising:
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- Analogical reasoning examples
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- Collaborative exploration dialogues
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- ELI5-style explanations
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- Enthusiastic encouragement patterns
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- Identity and persona consistency examples
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- Lateral thinking exercises
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- Playful humor and engagement
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- Research synthesis demonstrations
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### Training Configuration
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- **Epochs**: 2
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- **Batch Size**: 1 (effective: 16 with gradient accumulation)
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- **Learning Rate**: 2e-4
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- **Optimizer**: AdamW 8-bit
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- **Scheduler**: Cosine with 3% warmup
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- **Quantization**: 4-bit NF4 during training
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- **LoRA Configuration**: r=16, alpha=32, dropout=0.05
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- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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## Performance Characteristics
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### Strengths
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- Provides comprehensive, well-organized responses with clear structure
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- Excels at breaking down complex topics into digestible frameworks
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- Asks relevant clarifying questions to refine understanding
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- Maintains consistent persona and collaborative tone
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- Strong performance on educational and analytical tasks
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### Limitations
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- Response generation is approximately 5x slower than smaller specialized models
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- May provide more detail than necessary for simple queries
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- Academic/structured tone may not suit all conversational contexts
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- Inherits base model limitations regarding factual knowledge cutoff
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## Comparison with Atom-Ministral-8B
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| Feature | Atom-Olmo3-7B | Atom-Ministral-8B |
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|---------|---------------|-------------------|
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| License | Apache 2.0 | Mistral Research License |
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| Parameters | 7B | 8B |
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| Response Style | Structured, comprehensive | Conversational, concise |
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| Speed | ~29s average | ~6s average |
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| Best For | Deep analysis, education | Quick brainstorming, dialogue |
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| Commercial Use | Unrestricted | Restrictions apply |
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Both models share the same training philosophy and dataset but offer different trade-offs between depth and speed, making them complementary tools for different workflows.
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## Usage
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### Basic Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "vanta-research/atom-olmo3-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "system", "content": "You are Atom, an AI assistant made by VANTA Research in Portland, Oregon. You bring collaborative curiosity, playful enthusiasm, and thoughtful metaphors to every conversation."},
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{"role": "user", "content": "How might we use existing technology in unexpected ways to address climate change?"}
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]
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Recommended Parameters
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- **Temperature**: 0.7 (balanced creativity and coherence)
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- **Top-p**: 0.9 (nucleus sampling)
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- **Max Tokens**: 512-1024 (model tends toward comprehensive responses)
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- **Stop Sequences**: `<|im_start|>`, `<|im_end|>`
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## Ethical Considerations
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### Bias and Fairness
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This model inherits biases present in the Olmo-3 base model and training data. While efforts were made to curate balanced, high-quality training examples, users should:
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- Validate factual claims independently
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- Be aware of potential cultural and demographic biases
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- Apply appropriate safeguards for sensitive applications
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- Monitor outputs in production environments
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### Environmental Impact
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- **Training Hardware**: 1x NVIDIA RTX 3060 (12GB)
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- **Training Duration**: 5.9 hours
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- **Estimated Energy Consumption**: ~1.5 kWh
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- **Carbon Footprint**: Minimal (single GPU, short training duration)
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## License
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This model is released under the Apache License 2.0, providing broad permissions for commercial and non-commercial use. The base OLMo-3 model is also Apache 2.0 licensed.
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## Citation
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```bibtex
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@software{atom_olmo3_7b_2025,
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title = {Atom-OLMo3-7B: A Collaborative AI Assistant for Structured Problem-Solving},
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author = {VANTA Research},
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year = {2025},
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url = {https://huggingface.co/vanta-research/atom-olmo3-7b},
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note = {Fine-tuned from OLMo-3-7B-Instruct}
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}
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```
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## Acknowledgments
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Built on the Olmo-3-7B-Instruct model by the Allen Institute for AI (Ai2). Training infrastructure and methodology leverage the Hugging Face Transformers, TRL, and PEFT libraries.
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## Contact
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- Organization: hello@vantaresearch.xyz
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- Engineering/Design: tyler@vantaresearch.xyz
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---
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**Model Version**: 1.0
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**Release Date**: November 2025
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**Model Card Last Updated**: November 21, 2025
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*Proudly developed in Portland, Oregon by VANTA Research*
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16
chat_template.jinja
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chat_template.jinja
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{%- set has_system = messages|selectattr('role', 'equalto', 'system')|list|length > 0 -%}{%- if not has_system -%}{{- '<|im_start|>system
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You are a helpful function-calling AI assistant. ' -}}{%- if tools is none -%}{{- 'You do not currently have access to any functions. <functions></functions><|im_end|>
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' -}}{%- else -%}{{- 'You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions.' -}}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions><|im_end|>
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' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- '<|im_start|>system
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' + message['content'] -}}{%- if tools is not none -%}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions>' -}}{%- elif message.get('functions', none) is not none -%}{{- ' <functions>' + message['functions'] + '</functions>' -}}{%- endif -%}{{- '<|im_end|>
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' -}}{%- elif message['role'] == 'user' -%}{{- '<|im_start|>user
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' + message['content'] + '<|im_end|>
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' -}}{%- elif message['role'] == 'assistant' -%}{{- '<|im_start|>assistant
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' -}}{%- if message.get('content', none) is not none -%}{{- message['content'] -}}{%- endif -%}{%- if message.get('function_calls', none) is not none -%}{{- '<function_calls>' + message['function_calls'] + '</function_calls>' -}}{% elif message.get('tool_calls', none) is not none %}{{- '<function_calls>' -}}{%- for tool_call in message['tool_calls'] %}{%- if tool_call is mapping and tool_call.get('function', none) is not none %}{%- set args = tool_call['function']['arguments'] -%}{%- set ns = namespace(arguments_list=[]) -%}{%- for key, value in args.items() -%}{%- set ns.arguments_list = ns.arguments_list + [key ~ '=' ~ (value | tojson)] -%}{%- endfor -%}{%- set arguments = ns.arguments_list | join(', ') -%}{{- tool_call['function']['name'] + '(' + arguments + ')' -}}{%- if not loop.last -%}{{ '
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' }}{%- endif -%}{% else %}{{- tool_call -}}{%- endif %}{%- endfor %}{{- '</function_calls>' -}}{%- endif -%}{%- if not loop.last -%}{{- '<|im_end|>' + '
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' -}}{%- else -%}{{- eos_token -}}{%- endif -%}{%- elif message['role'] == 'environment' -%}{{- '<|im_start|>environment
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' + message['content'] + '<|im_end|>
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' -}}{%- elif message['role'] == 'tool' -%}{{- '<|im_start|>environment
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' + message['content'] + '<|im_end|>
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' -}}{%- endif -%}{%- if loop.last and add_generation_prompt -%}{{- '<|im_start|>assistant
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' -}}{%- endif -%}{%- endfor -%}
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config.json
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config.json
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{
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"architectures": [
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"Olmo3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"dtype": "float16",
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"eos_token_id": 100257,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"layer_types": [
|
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
|
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"sliding_attention",
|
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"sliding_attention",
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"sliding_attention",
|
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"full_attention",
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"sliding_attention",
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"sliding_attention",
|
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"sliding_attention",
|
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"full_attention"
|
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],
|
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"max_position_embeddings": 65536,
|
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"model_type": "olmo3",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 100277,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
|
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"attention_factor": 1.2079441541679836,
|
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"beta_fast": 32,
|
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"beta_slow": 1,
|
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"factor": 8.0,
|
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"original_max_position_embeddings": 8192,
|
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"rope_type": "yarn"
|
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},
|
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"rope_theta": 500000,
|
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"sliding_window": 4096,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "4.57.1",
|
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"use_cache": false,
|
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"vocab_size": 100278
|
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}
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generation_config.json
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{
|
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"_from_model_config": true,
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"eos_token_id": [
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|
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"transformers_version": "4.57.1"
|
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}
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100001
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30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
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||||
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||||
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||||
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|
||||
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|
||||
500524
tokenizer.json
Normal file
500524
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
189
tokenizer_config.json
Normal file
189
tokenizer_config.json
Normal file
@@ -0,0 +1,189 @@
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100263": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100267": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
"100271": {
|
||||
"content": "<|extra_id_2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100272": {
|
||||
"content": "<|extra_id_3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100273": {
|
||||
"content": "<|extra_id_4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100274": {
|
||||
"content": "<|extra_id_5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100275": {
|
||||
"content": "<|extra_id_6|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100276": {
|
||||
"content": "<|endofprompt|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100277": {
|
||||
"content": "<|pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 65536,
|
||||
"pad_token": "<|pad|>",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
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