| --- |
| license: mit |
| language: |
| - en |
| - hi |
| base_model: |
| - meta-llama/Llama-3.2-1B |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - llama |
| - mini |
| --- |
| |
| # Type-o1-mini-instruct |
|
|
| A compact general-purpose instruct model designed for everyday assistant use across a wide range of domains — from science and math to writing, coding, language tasks, and tool-style web search workflows. |
|
|
| The model is intended for lightweight assistant use cases where users need clear, well-structured answers, helpful explanations, and practical support across many subject areas. |
|
|
| ## Capabilities |
|
|
| This model can help with: |
|
|
| * General chat and multi-turn conversation |
| * Biology, chemistry, and physics questions and explanations |
| * Mathematics and quantitative reasoning |
| * Engineering concepts and explanations |
| * Health and medical information (general, non-clinical) |
| * Python coding assistance and code explanation |
| * Creative writing (stories, poetry, writing prompts) |
| * Content generation (marketing copy, social media captions, emails) |
| * English grammar correction and rewriting |
| * Advanced NLP tasks: |
| * Fill-mask |
| * Table question answering |
| * Context-based question answering (SQuAD style) |
| * Summarization (dialogue, news, and scientific papers) |
| * English ↔ Hindi translation |
| * School and coursework-level question answering |
| * Web search tool-call style conversations |
|
|
| ## Chat Format |
|
|
| The model follows a Harmony-style chat structure. |
|
|
| Supported interaction flow: |
|
|
| ```text |
| system -> developer -> user -> tool call -> tool result -> final response |
| ``` |
|
|
| For normal chat use, you can use a standard chat-template style prompt. |
|
|
| ## Web Search Tool-Call Style |
|
|
| 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. |
|
|
| Example structure: |
|
|
| ```text |
| system: You are a helpful assistant with access to web search. |
| user: Find the latest information about a topic. |
| assistant tool call: web_search(...) |
| tool result: ... |
| assistant final: Answer using the search result. |
| ``` |
|
|
| Actual tool execution depends on your inference framework or application wrapper. |
|
|
| ## Recommended Use Cases |
|
|
| This model is best suited for: |
|
|
| * General-purpose lightweight assistants |
| * Study and homework helpers across science subjects |
| * Writing and content generation helpers |
| * Grammar and language correction tools |
| * English ↔ Hindi translation helpers |
| * Summarization and document Q&A tools |
| * Beginner Python learning assistants |
| * Tool-call research experiments |
| * Chatbots that need broad domain coverage in a small model |
|
|
| ## Limitations |
|
|
| This model is not recommended for: |
|
|
| * Production-critical software generation without review |
| * Non-Python coding tasks such as C++, Java, Rust, Go, or JavaScript |
| * Security-sensitive code generation |
| * Medical, legal, or financial decision-making |
| * Advanced research-level science or mathematics |
| * Long multi-file software engineering tasks |
| * Tasks requiring very long context |
| * High-stakes factual lookup without verification |
|
|
| The model may sometimes: |
|
|
| * Produce incorrect facts or reasoning |
| * Miss edge cases |
| * Over-explain simple questions |
| * Generate code that needs testing |
| * Struggle with very long context |
| * Use tool-call format inconsistently depending on the prompt |
| * Give uneven quality across its many supported domains |
|
|
| Always verify important outputs and test generated code before using it. |
|
|
| ## License |
|
|
| Please check the model repository license before commercial or production use. |
|
|
| ## Disclaimer |
|
|
| 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. |
