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--- |
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library_name: transformers |
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license: mit |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-Coder-1.5B |
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pipeline_tag: text-generation |
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tags: |
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- code |
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--- |
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🧩 Argo — Fine-Tuned Qwen 2.5-Coder-1.5B for JavaScript, React, and Node.js |
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Model name: jamesmeike/argo |
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Base model: Qwen/Qwen 2.5-Coder-1.5B |
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Language(s): JavaScript (React, Node.js, Express, JSX, TSX) |
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Framework: 🤗 Transformers, PEFT (LoRA), BitsAndBytes (16-bit quantization) |
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--- |
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🧠 Model Overview |
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Argo is a fine-tuned version of Qwen 2.5-Coder-1.5, optimized for modern JavaScript development, including React, Node.js, Express, and Next.js codebases. |
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It aims to generate high-quality, framework-aware code completions, refactors, and full function templates while maintaining code style and syntax accuracy. |
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--- |
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🎯 Objective |
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The model was trained to: |
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Autocomplete and generate React components. |
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Build Express.js/Node.js APIs. |
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Assist in TypeScript or JavaScript module creation. |
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Write utility functions and middleware templates. |
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Understand JSX and TSX patterns. |
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--- |
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⚙️ Technical Details |
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Category Details |
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Base Model Qwen/Qwen 2.5-Coder-1.5B |
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Fine-Tuning Framework PEFT + LoRA |
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Precision 16-bit (bitsandbytes) |
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Training Duration 5 epochs |
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Batch Size 1 (gradient_accumulation=16) |
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Dataset Filtered subset of Nan-Do/code-search-net-javascript |
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Total Samples ~3,000 |
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Tokenizer AutoTokenizer (from base model) |
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--- |
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🧾 Training Process |
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The dataset was filtered for JavaScript samples containing: |
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> react, jsx, tsx, express, node, nextjs |
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Each entry was tokenized with a maximum length of 512 tokens. |
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Training was performed using Hugging Face Transformers Trainer with LoRA fine-tuning and 8-bit quantization for GPU efficiency. |
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After training, LoRA weights were merged into the base Qwen 2.5-Coder-1.5B model using: |
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model = model.merge_and_unload() |
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--- |
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🧪 Example Usage |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "jamesmeike/argo" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") |
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prompt = "// Create a simple Express.js API that returns 'Hello Argo!'" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=120, temperature=0.7) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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--- |
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🧩 Example Output |
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const express = require("express"); |
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const app = express(); |
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app.get("/", (req, res) => { |
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res.send("Hello Argo!"); |
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}); |
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app.listen(3000, () => console.log("Server running on port 3000")); |
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--- |
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⚠️ Limitations |
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May produce incomplete or partially correct code in long generations. |
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Not tested for security vulnerabilities or dependency management. |
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May occasionally hallucinate imports or libraries. |
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Intended for educational and research purposes. |
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--- |
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📚 Intended Use |
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Argo is designed for: |
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JavaScript/React/Node.js code generation. |
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Developer assistance in code completion. |
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Educational fine-tuning reference for StarCoder models. |
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Not intended for production-critical or private code generation. |
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--- |
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🏷️ Citation |
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If you use this model, please cite: |
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@model{jamesmeike/argo, |
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title = {Argo: A Fine-Tuned Qwen 2.5-Coder-1.5B Model for JavaScript and React}, |
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author = {Jamesmeike}, |
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year = {2025}, |
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url = {https://huggingface.co/jamesmeike/argo} |
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} |
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--- |
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🙌 Acknowledgements |
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Qwen for Qwen 2.5-Coder-1.5B |
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Hugging Face for Transformers, PEFT, and Datasets |
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Nan-Do/code-search-net-javascript for the open dataset |
