---
base_model: unsloth/Qwen3-4B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen
- trl
- sft
license: apache-2.0
language:
- en
---

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<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>
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