113 lines
3.6 KiB
Markdown
113 lines
3.6 KiB
Markdown
---
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license: mit
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language:
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- en
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- hi
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base_model:
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- Qwen/Qwen2.5-0.5B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- qwen
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- nano
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---
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# Type-o1-nano-instruct
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A very compact general-purpose instruct model designed for lightweight assistant use across everyday tasks — general chat, Python coding help, writing and content generation, language tasks, math, and tool-style web search workflows.
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The model is intended for small-footprint deployments where users need clear, practical answers and helpful explanations without the cost of a larger model.
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## Capabilities
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This model can help with:
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* General chat and multi-turn conversation
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* Python coding assistance and code explanation
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* Mathematics and basic quantitative reasoning
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* Engineering concepts and explanations
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* Creative writing (stories, poetry, writing prompts)
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* Content generation (marketing copy, social media captions, emails)
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* English grammar correction and rewriting
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* Advanced NLP tasks:
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* Fill-mask
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* Table question answering
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* Context-based question answering (SQuAD style)
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* Summarization (dialogue, news, and scientific papers)
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* English ↔ Hindi translation
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* Web search tool-call style conversations
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## Chat Format
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The model follows a Harmony-style chat structure.
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Supported interaction flow:
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```text
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system -> developer -> user -> tool call -> tool result -> final response
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```
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For normal chat use, you can use a standard chat-template style prompt.
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## Web Search Tool-Call Style
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The model can be used in tool-calling style conversations where the assistant decides when a search is needed, emits a tool call, receives a tool result, and then writes the final answer.
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Example structure:
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```text
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system: You are a helpful assistant with access to web search.
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user: Find the latest information about a topic.
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assistant tool call: web_search(...)
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tool result: ...
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assistant final: Answer using the search result.
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```
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Actual tool execution depends on your inference framework or application wrapper.
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## Recommended Use Cases
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This model is best suited for:
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* Lightweight general-purpose assistants
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* On-device or low-resource deployments
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* Writing and content generation helpers
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* Grammar and language correction tools
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* English ↔ Hindi translation helpers
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* Summarization and document Q&A tools
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* Beginner Python learning assistants
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* Tool-call research experiments
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* Chatbots where speed and small size matter more than depth
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## Limitations
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This model is not recommended for:
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* Production-critical software generation without review
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* Non-Python coding tasks such as C++, Java, Rust, Go, or JavaScript
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* Security-sensitive code generation
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* Medical, legal, or financial decision-making
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* Advanced or research-level mathematics
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* Long multi-file software engineering tasks
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* Tasks requiring very long context
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* High-stakes factual lookup without verification
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The model may sometimes:
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* Produce incorrect facts or reasoning
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* Miss edge cases
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* Over-explain simple questions
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* Generate code that needs testing
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* Struggle with very long context
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* Use tool-call format inconsistently depending on the prompt
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* Give uneven quality across its supported domains
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Being a very small model, it is best used for straightforward tasks rather than complex or nuanced ones. Always verify important outputs and test generated code before using it.
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## License
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Please check the model repository license before commercial or production use.
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## Disclaimer
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This model is an experimental small general-purpose assistant. It should be used as a helpful assistant, not as a guaranteed source of truth. For important tasks, verify outputs with tests, documentation, and human review. |