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.
The model is intended for small-footprint deployments where users need clear, practical answers and helpful explanations without the cost of a larger model.
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
Web search tool-call style conversations
Chat Format
The model follows a Harmony-style chat structure.
Supported interaction flow:
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
1system: You are a helpful assistant with access to web search.
2user: Find the latest information about a topic.
3assistant tool call: web_search(...)
4tool result: ...
5assistant 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:
Lightweight general-purpose assistants
On-device or low-resource deployments
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 where speed and small size matter more than depth
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 or research-level 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 supported domains
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.
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.
