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README.md

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----
-license: mit
----
+---
+license: mit
+library_name: transformers
+datasets:
+- md-nishat-008/Mojo-Corpus
+- md-nishat-008/Mojo-SFT
+- md-nishat-008/Mojo-mSFT
+pipeline_tag: text-generation
+---
+
+<div align="center">
+<h1>🔥 Mojo-Coder 🔥</h1>
+<em>State-of-the-art Language Model for Mojo Programming</em>
+</div>
+
+
+<div align="center">
+<table><tr>
+<td><a href="https://arxiv.org/abs/2410.17736"><img src="https://img.shields.io/badge/arXiv-Read_Paper-blue?style=for-the-badge&logo=arxiv" /></a></td>
+<td><a href="mailto:[email protected]"><img src="https://img.shields.io/badge/Email-Contact_Us-blue?style=for-the-badge&logo=gmail" /></a></td>
+</tr></table>
+</div>
+
+
+
+
+<div align="center">
+<h2>🎯 Background and Motivation</h2>
+</div>
+
+Mojo programming language, developed by Modular, has emerged as a game-changing technology in high-performance computing and AI development. Despite its growing popularity and impressive capabilities (up to 68,000x faster than Python!), existing LLMs struggle with Mojo code generation. Mojo-Coder addresses this gap by providing specialized support for Mojo programming, built upon the robust architecture of [CodeGemma-7B-IT](https://huggingface.co/google/codegemma-7b-it/).
+
+<div align="center">
+<h2>🤖 Model Information</h2>
+</div>
+
+Mojo-Coder transforms natural language instructions into optimized Mojo code, supporting multiple languages (English, German, French, Spanish, and Bangla) while maintaining high-quality code generation capabilities.
+
+<div align="center">
+<h2>📝 Description</h2>
+</div>
+
+The Mojo-Coder family consists of three specialized 7B-parameter models, each built on CodeGemma's architecture:
+
+|                            | [mojo-coder](https://huggingface.co/md-nishat-008/mojo-coder) | [mojo-coder-it](https://huggingface.co/md-nishat-008/mojo-coder-it) | [**mojo-coder-it-m**](https://huggingface.co/md-nishat-008/mojo-coder-it-m) |
+|---------------------------|:---:|:---:|:---:|
+| 🔄 Code Completion        | ✅ | ✅ | ✅ |
+| 💡 NL → Code Generation   |    | ✅ | ✅ |
+| 🌏 Multilingual Support   |    |    | ✅ |
+| 📝 Instruction Following  |    | ✅ | ✅ |
+
+<div align="center">
+<h2>🚀 Sample Usage</h2>
+</div>
+
+Choose the model that best fits your needs:
+- For basic Mojo code completion: [mojo-coder](https://huggingface.co/md-nishat-008/mojo-coder)
+- For English instruction-based code generation: [mojo-coder-it](https://huggingface.co/md-nishat-008/mojo-coder-it)
+- For multilingual support: [mojo-coder-it-m](https://huggingface.co/md-nishat-008/mojo-coder-it-m)
+
+Notably, our models significantly outperform current state-of-the-art models including GPT-4o and Claude-3.5-Sonnet on the HumanEval-Mojo benchmark.
+
+<div align="center">
+<h3>✨ Let's revolutionize Mojo programming together! ✨</h3>
+</div>
+
+
+
+
+<div style="color: red; text-align: center; padding: 10px; margin: 20px 0; border: 2px solid red; border-radius: 5px;">
+<strong>⚠️ IMPORTANT: When using the model, you MUST explicitly mention "Mojo" in your prompts (e.g., "Write a Mojo function to...", "Create Mojo code that...") otherwise the model may not generate Mojo code!</strong>
+</div>
+
+#### For Code Generation
+
+```python
+from transformers import GemmaTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("md-nishat-008/Mojo-Coder-it")
+model = AutoModelForCausalLM.from_pretrained("md-nishat-008/Mojo-Coder-it")
+
+input_text = "Write me a Mojo function to calculate the nth fibonacci number."
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+#### Chat Template
+
+The instruction-tuned models use a chat template that must be adhered to for conversational use.
+The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
+
+Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("md-nishat-008/Mojo-Coder-it")
+model = AutoModelForCausalLM.from_pretrained("md-nishat-008/Mojo-Coder-it")
+
+chat = [{"role": "user", "content": "Write a function that calculates factorial of a number in Mojo"}]
+inputs = tokenizer.apply_chat_template(chat, tokenize=True, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model.generate(
+        inputs=inputs,
+        max_new_tokens=1000,
+        temperature=0.7,
+        top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id
+    )
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+At this point, the prompt contains the following text:
+
+```
+<bos><start_of_turn>user
+Write a hello world program in Mojo<end_of_turn>
+<start_of_turn>model
+```
+
+As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
+(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
+the `<end_of_turn>` token.
+
+You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
+chat template.
+
+After the prompt is ready, generation can be performed like this:
+
+```py
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
+```
+
+<div align="center">
+<h2>⚙️ Inputs and Outputs</h2>
+</div>
+
+**Inputs**:
+- For base model (mojo-coder): code prefix and/or suffix for Mojo code completion
+- For instruction-tuned models (mojo-coder-it & mojo-coder-it-m): natural language prompts/instructions
+
+<p style="color: red;"><strong>Note: In prompts, you must explicitly mention "Mojo" (e.g., "Write a Mojo function to...", "Write Mojo code to...") otherwise the models may not generate Mojo code.</strong></p>
+
+**Outputs**:
+- For all variants: Mojo code snippets and natural language responses
+- Additional explanations and documentation when requested
+
+<div align="center">
+<h2>📚 Model Data</h2>
+</div>
+
+### Training Dataset
+
+Using [CodeGemma-7B-IT](https://huggingface.co/google/codegemma-7b-it/) as our base model, we further trained on:
+- [Mojo-Corpus](https://huggingface.co/datasets/md-nishat-008/Mojo_Corpus): 6.5M tokens of curated Mojo code from public repositories
+- [Mojo-SFT](https://huggingface.co/datasets/md-nishat-008/Mojo_SFT): 3,200 instruction-code pairs for English
+- [Mojo-mSFT](https://huggingface.co/datasets/md-nishat-008/Mojo_mSFT): Multilingual instruction-code pairs in 5 languages
+
+### Training Data Processing
+
+The following data pre-processing techniques were applied:
+- Rigorous filtering pipeline (F1-F6) to ensure code quality
+- Apache 2.0 license compliance
+- Language detection using fastText
+- Duplicate removal and content validation
+- Expert review for instruction-code pairs
+
+<div align="center">
+<h2>📊 Evaluation Information</h2>
+</div>
+
+### Evaluation Approach
+
+We evaluate Mojo-Coder on:
+- [HumanEval-Mojo](https://huggingface.co/datasets/md-nishat-008/HumanEval-Mojo): First benchmark for Mojo code generation
+- Multi-language instruction following
+- Code quality and execution success
+
+### Evaluation Results
+
+#### Code Generation Benchmarks (Pass@1)
+
+| Model | HumanEval-Mojo |
+|-------|----------------|
+| GPT-4o | 25.5% |
+| Claude-3.5-Sonnet | 39.8% |
+| mojo-coder | 36.7% |
+| mojo-coder-it-m | 61.5% |
+| mojo-coder-it | 66.4% |
+
+<div align="center">
+<h2>⚠️ Limitations and Usage</h2>
+</div>
+
+### Intended Usage
+- Mojo code completion and generation
+- Multi-language instruction following
+- Code documentation and explanation
+- Educational support for Mojo programming
+
+### Known Limitations
+- Limited to Mojo programming language
+- Requires explicit mention of "Mojo" in prompts
+- Performance may vary with complex algorithms
+- May occasionally generate Python-like syntax
+- Based on data available up to 2024
+
+### Ethical Considerations
+The model is designed for:
+- Educational and development purposes
+- Open-source contribution to Mojo ecosystem
+- Supporting multilingual access to Mojo programming
+
+Code should be reviewed and tested before production use, especially for performance-critical applications.