Developer-pintu/my-hinglish-coder-ai

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Developer-pintu/my-hinglish-coder-ai is a 1.5 billion parameter Qwen2-based instruction-tuned causal language model developed by Developer-pintu. Finetuned using Unsloth and Huggingface's TRL library, this model is optimized for coding tasks. It features a 32768-token context length, making it suitable for processing substantial codebases and complex programming instructions. Its primary strength lies in code generation and understanding, particularly in a Hinglish context.

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Model Overview

Developer-pintu/my-hinglish-coder-ai is a 1.5 billion parameter instruction-tuned language model, finetuned by Developer-pintu. It is based on the Qwen2 architecture and was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster finetuning process. The model supports a substantial context length of 32768 tokens, allowing it to handle extensive inputs and maintain coherence over long interactions.

Key Capabilities

  • Code Generation: Optimized for generating code, likely across various programming languages.
  • Instruction Following: Designed to accurately follow user instructions, making it suitable for interactive coding assistance.
  • Efficient Training: Benefits from Unsloth's optimizations, indicating a focus on performance and resource efficiency during development.

Good For

  • Coding Assistance: Developers seeking a model to help with code generation, completion, and debugging.
  • Hinglish Contexts: While not explicitly stated in the provided README, the model name "my-hinglish-coder-ai" suggests potential specialization or fine-tuning for Hinglish (Hindi + English) programming contexts, which would be a unique differentiator.
  • Research and Development: As a finetuned model, it serves as a strong base for further experimentation and adaptation to specific coding challenges.