GRMR-V3-G4B

<html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> </head> <div class="container"> <h1>GRMR-V3-G4B</h1> <p>GRMR-V3-G4B is a fine-tuned version of <a href="https://huggingface.co/unsloth/gemma-3-4b-pt">unsloth/gemma-3-4b-pt</a> specifically optimized for grammar correction tasks.</p> <div class="important-note"> <p><strong>IMPORTANT:</strong> Please ensure you are using the following sampler settings for optimal results:</p> <pre><code>temperature = 0.7 frequency_penalty = 0.0 presence_penalty = 0.0 min_p = 0.01 top_p = 0.95 top_k = 40</code></pre> </div> <h2>Model description</h2> <p>GRMR-V3-G4B is a grammar correction model built on Gemma 3 4B (base model). It has been fine-tuned on a large dataset of grammar correction examples to help improve text quality by fixing grammatical errors, punctuation, spelling, and other language issues.</p> <p>The model uses a specialized chat template that structures inputs as "text" and outputs as "corrected" to maintain a clear distinction between original and corrected content.</p> <h3>Model Details</h3> <div class="model-meta"> <p><strong>Developed by:</strong> qingy2024</p> <p><strong>Finetuned from model:</strong> <a href="https://huggingface.co/unsloth/gemma-3-4b-pt">unsloth/gemma-3-4b-pt</a> <p><strong>Model type:</strong> Gemma 3</p> <p><strong>Language(s):</strong> English</p> <p><strong>License:</strong> apache-2.0</p> <p><strong>Training Dataset:</strong> <a href="https://huggingface.co/datasets/qingy2024/grmr-v4-60k">qingy2024/grmr-v4-60k</a></p> </div> <hr> <h2>Manual Testing Examples</h2> <p>Here are a few examples of grammar corrections this model can handle:</p> <table> <thead> <tr> <th>Original Text</th> <th>Corrected Text</th> </tr> </thead> <tbody> <tr> <td>i dont know weather to bring a umbrella today</td> <td>I don't know whether to bring an umbrella today.</td> </tr> <tr> <td>she go to the store yesterday</td> <td>She went to the store yesterday.</td> </tr> <tr> <td>they is going to be late for the meeting</td> <td>They are going to be late for the meeting.</td> </tr> <tr> <td>the cat laying on the floor all day</td> <td>The cat is laying on the floor all day.</td> </tr> </tbody> </table> <hr> <h2>Training procedure</h2> <p>The model was fine-tuned using full parameter fine-tuning (not LoRA) on the GRMR-V4-60K dataset. The training was optimized using the Unsloth framework for efficient training of LLMs.</p> <h3>Training hyperparameters</h3> <ul> <li><strong>Batch size:</strong> 8</li> <li><strong>Gradient accumulation steps:</strong> 2</li> <li><strong>Learning rate:</strong> 5e-5</li> <li><strong>Epochs:</strong> 1</li> <li><strong>Optimizer:</strong> AdamW (8-bit)</li> <li><strong>Weight decay:</strong> 0.01</li> <li><strong>LR scheduler:</strong> Cosine</li> <li><strong>Warmup steps:</strong> 180</li> <li><strong>Max sequence length:</strong> 16,384</li> <li><strong>Training precision:</strong> Mixed precision (BF16 where available, FP16 otherwise)</li> </ul> <h2>Intended uses & limitations</h2> <p>This model is designed for grammar correction tasks. It can be used to:</p> <ul> <li>Fix grammatical errors in written text</li> <li>Correct punctuation</li> <li>Address spelling mistakes</li> <li>Improve sentence structure and clarity</li> </ul> <h3>Limitations</h3> <ul> <li>The model may struggle with highly technical or domain-specific content</li> <li>It may not fully understand context-dependent grammar rules in all cases</li> <li>Performance may vary for non-standard English or text with multiple errors</li> </ul> <h2>How to use</h2> <p><code>llama.cpp</code> and projects based on it should be able to run this model like any others.</p> <p>For pure <code>transformers</code> code, you can refer here:</p> <pre><code class="language-python">from transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizer model_name = "qingy2024/GRMR-V3-G4B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) text_to_correct = "i am going to the store tommorow and buy some thing for dinner" messages = [ {"role": "user", "content": text_to_correct} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs["input_ids"], max_new_tokens=512, temperature=0.1, # NOTE: For best results, use the recommended temperature of 0.7 do_sample=True ) corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(corrected_text)</code></pre> <h3>Using with the Hugging Face pipeline</h3><pre><code class="language-python">from transformers import pipeline pipe = pipeline( "text-generation", model="qingy2024/GRMR-V3-G4B", torch_dtype="auto", device_map="auto" ) messages = [ {"role": "user", "content": "i dont know weather to bring a umbrella today"} ] result = pipe( messages, max_new_tokens=100, temperature=0.1, # NOTE: For best results, use the recommended temperature of 0.7 do_sample=True, return_full_text=False )[0]["generated_text"] print(result)</code></pre><p><em>Note: The Python examples above use <code>temperature=0.1</code> for reproducibility in quick tests. For optimal grammar correction quality, please use the recommended sampler settings, especially <code>temperature=0.7</code>.</em></p><h2>Custom Chat Template</h2><p class="chat-template-info">The model uses a custom chat template with special formatting for grammar correction:</p><ul><li>User inputs are formatted with <code><|text_start|></code> and <code><|text_end|></code> tags</li><li>Model outputs are formatted with <code><|corrected_start|></code> and <code><|corrected_end|></code> tags</li></ul><p>The complete chat template is:</p><pre><code class="language-jinja">{{- bos_token }} {#- Process messages with role mapping #} {%- for message in messages %} {%- if message['role'] == 'user' %} {{- '<start_of_turn>text '+ message['content'] | trim + '<end_of_turn> ' }} {%- elif message['role'] == 'assistant' %} {{- '<start_of_turn>corrected '+ message['content'] | trim + '<end_of_turn> ' }} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<start_of_turn>corrected ' }} {%- endif %}</code></pre><h2>Training Dataset</h2><p>The model was fine-tuned on the <a href="https://huggingface.co/datasets/qingy2024/grmr-v4-60k">qingy2024/grmr-v4-60k</a> dataset, which contains 60,000 examples of original text and their grammatically corrected versions.</p><h2>Bias, Risks, and Limitations</h2><ul><li>The model may reflect biases present in the training data</li><li>It may not perform equally well across different writing styles or domains</li><li>The model might occasionally introduce errors or change the meaning of text</li><li>It focuses on grammatical correctness rather than stylistic improvements</li></ul><h2>Citations</h2><pre><code>@article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} }</code></pre><h2>Contact</h2><p>For questions or issues related to the model, please reach out via Hugging Face or by creating an issue in the repository.</p></div> <style> body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; line-height: 1.6; margin: 0; padding: 0; background-color: #f8f9fa; color: #333; } .container { max-width: 1200px; margin: 10px auto; padding: 25px; background-color: #ffffff; border-radius: 8px; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08); } h1, h2, h3 { color: #0056b3; /* Primary Blue */ margin-top: 1.5em; margin-bottom: 0.7em; } h1 { text-align: center; font-size: 2.2em; border-bottom: 2px solid #e0e0e0; padding-bottom: 0.5em; margin-top: 0; } h2 { font-size: 1.8em; border-bottom: 1px solid #e9ecef; padding-bottom: 0.3em; } h3 { font-size: 1.4em; color: #007bff; /* Lighter Blue for sub-headings */ } p, li { font-size: 1em; color: #555; } a { color: #007bff; text-decoration: none; } a:hover { text-decoration: underline; color: #0056b3; } .important-note { background-color: #e7f3ff; /* Light blue background */ border-left: 5px solid #007bff; /* Blue accent border */ margin: 20px 0px; border-radius: 5px; } .important-note strong { color: #0056b3; font-weight: 600; } .important-note { background-color: #d0e8ff; padding: 0.05em 1.0em; border-radius: 3px; font-size: 0.9em; } code { padding: 0.1em 0.4em; border-radius: 3px; font-size: 0.9em; } table { width: 100%; border-collapse: collapse; margin: 20px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.05); } th, td { border: 1px solid #dee2e6; padding: 10px 12px; text-align: left; vertical-align: top; } th { background-color: #e9ecef; /* Light gray for headers */ font-weight: 600; color: #212529; } td:first-child { /* font-style: italic; */ color: #444; } pre { background-color: #f1f3f5; padding: 15px; border-radius: 5px; overflow-x: auto; border: 1px solid #ced4da; font-size: 0.9em; } code { font-family: "SFMono-Regular", Consolas, "Liberation Mono", Menlo, Courier, monospace; background-color: #e9ecef; padding: 0.2em 0.4em; border-radius: 3px; font-size: 0.9em; } pre code { background-color: transparent; padding: 0; border-radius: 0; font-size: 1em; } ul { padding-left: 20px; } li { margin-bottom: 0.5em; } hr { border: none; border-top: 1px solid #e0e0e0; margin: 30px 0; } .model-meta { background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin-bottom: 20px; border: 1px solid #e9ecef; } .model-meta p { margin-bottom: 0.5em; } .model-meta strong { color: #333; } /* Specific styling for chat template explanation */ .chat-template-info span { font-weight: bold; color: #0056b3; } </style>