GRMR-V3-L1B

<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-L1B</h1> <p>GRMR-V3-L1B is a fine-tuned version of <a href="https://huggingface.co/unsloth/Llama-3.2-1B">unsloth/Llama-3.2-1B</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-L1B is a grammar correction model built on Meta's Llama 3.2 1B 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/Llama-3.2-1B">unsloth/Llama-3.2-1B</a></p> <p><strong>Model type:</strong> Llama 3.2</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-L1B" 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-L1B", 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><|start_header_id|>text<|end_header_id|></code> headers</li> <li>Model outputs are formatted with <code><|start_header_id|>corrected<|end_header_id|></code> headers</li> <li>Messages are separated by <code><|eot_id|></code> tokens</li> <li>The chat template should work without any extra tweaking in vLLM or <code>llama.cpp</code>.</li> </ul> <p>An example of the applied template structure might look like:</p> <pre><code><|begin_of_text|><|start_header_id|>text<|end_header_id|>

i dont know weather to bring a umbrella today<|eot_id|><|start_header_id|>corrected<|end_header_id|>

I don't know whether to bring an umbrella today.<|eot_id|></code></pre> <p>(When generating, you would only provide the input up to <code><|start_header_id|>corrected<|end_header_id|>\n\n</code>)</p> <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>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>

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