GRMR-V3-Q4B

<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-Q4B</h1><p>GRMR-V3-Q4B is a fine-tuned version of <a href="https://huggingface.co/unsloth/Qwen3-4B-Base">unsloth/Qwen3-4B-Base</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-Q4B is a grammar correction model built on Qwen3 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/Qwen3-4B-Base">unsloth/Qwen3-4B-Base</a></p><p><strong>Model type:</strong> Qwen3</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>Projects based on Hugging Face transformers should be able to run this model easily.</p><pre><code class="language-python">from transformers import AutoModelForCausalLM, AutoTokenizer # Load model and tokenizer model_name = "qingy2024/GRMR-V3-Q4B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Text with grammar errors to correct text_to_correct = "i am going to the store tommorow and buy some thing for dinner" # Format as messages messages = [ {"role": "user", "content": text_to_correct} ] # Apply the custom chat template prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # Tokenize and generate inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs["input_ids"], max_new_tokens=512, temperature=0.1, do_sample=True ) # Decode and print the corrected text 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-Q4B", 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, do_sample=True, return_full_text=False )[0]["generated_text"] print(result)</code></pre><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">{%- for message in messages %} {%- if message.role == "user" %} {{- '<|text_start|>\n' + message.content + '<|text_end|>\n' }} {%- elif message.role == "assistant" %} {{- '<|corrected_start|>\n' + message.content + '<|corrected_end|>\n' }} {%- else %} {{- raise('Unknown role: ' + message.role) }} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<|corrected_start|>\n' }} {%- 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>@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }</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"; 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