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Language-Based Model

Our model for translation, Q&A, multilingual tasks, and reasoning across languages.

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Written by Niko McCarty
Updated this week

The Language-Based model is designed for broad language understanding and generation. It excels at tasks such as:

  • Translation: converting text between multiple languages.

  • Summarization: condensing long passages into shorter, more digestible text.

  • Conversation: engaging in open-ended dialogue.

  • Instruction Following: adapting responses based on prompts or directions.

  • Structured Data Extraction: pulling clean, formatted information from unstructured text.

This model is best for language-heavy tasks that require nuanced comprehension, multilingual support, and longer context handling.

Model Specs

  • # of Parameters: ~600M

  • Format Type: causal_lm

  • Download Size: ~596 MB (estimate)

  • Max Sequence Length: 32,768 tokens

  • Context Window: 32,768 tokens

With its extended context window, this model is well-suited for analyzing or generating long passages of text while keeping track of broader context.

How to Use

Step 1: Choose the model

  • From the Models tab, select the Language-Based model when creating a new fine-tuned model.

Step 2: Prepare your dataset

  • Use fields like Instruction, Input, and Response.

  • Example:

    • Instruction: “Translate this text from English to Spanish.”

    • Input: “The weather is beautiful today.”

    • Response: “El clima es hermoso hoy.”

Step 3: Train your model

  • Upload your dataset in CSV, Excel, JSON, or JSON-L (all converted automatically to JSON-L).

  • Start fine-tuning; training uses LoRA for efficiency.

Step 4: Test and refine

  • On the Model Details page, chat directly with your model in the browser.

  • Adjust Temperature (randomness) and Max Tokens (output length) to shape responses.

  • If results aren’t aligned with your needs, refine your dataset and retrain.

Step 5: Deploy or download

  • Download in GGUF format for local or edge-device deployment.

  • Or deploy instantly with Minibase Cloud and send API requests from your apps.

Tips & Best Practices

  1. Take advantage of the large context window: With support for up to 32k tokens, this model is ideal for long documents, multi-turn conversations, or analyzing extended inputs.

  2. Experiment with instructions: The model supports embedded instructions directly in the prompt—make them clear and consistent for best results.

  3. Balance speed and quality with quantization options:

    1. High (Q8_0): near-lossless, production-grade quality.

    2. Medium (Q4_K_M): balanced performance and efficiency.

    3. Low (Q4_K_S): compact, optimized for mobile or edge devices.

Troubleshooting

The model truncates my input

Ensure your text is under the maximum sequence length of 32,768 tokens. Longer inputs will be cut off.

Outputs feel inconsistent

Refine instructions in your dataset and ensure they match how you plan to prompt the model. Consistency improves results.

Training is slow

Larger models take longer to train, especially with large datasets. Use smaller subsets for quick iterations, then scale up.

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