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--- |
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base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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- sw |
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datasets: |
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- wikimedia/wikipedia |
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- Mollel/alpaca-swahili |
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- Mollel/swahili_pretrain_data |
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library_name: peft |
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--- |
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# Model description |
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The model can be used for Swahili language generation, translation, and other NLP tasks, especially focused on the pretraining and fine-tuning domains. |
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It has been pre-trained and fine-tuned specifically for Swahili language tasks with the Unsloth framework. |
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This is a development version and it's not recommended for general use. |
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- **Developed by:** calcpy |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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## Out-of-Scope Use |
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The model is not designed for tasks outside of the Swahili language |
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or tasks requiring highly factual precision in domains not covered by the training datasets. |
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## Bias, Risks, and Limitations |
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The model inherits any potential biases present in the Swahili Wikipedia and Mollel's dataset. Users should be cautious when applying this model to sensitive applications. |
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### Recommendations |
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Users should perform bias evaluations specific to their use case and ensure that any downstream applications consider potential ethical implications. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("path_to_your_model") |
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tokenizer = AutoTokenizer.from_pretrained("path_to_your_model") |
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# Example inference |
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instruction = "Endelea mlolongo wa fibonacci:" |
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input_data = "1, 1, 2, 3, 5, 8," |
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prompt = f"Chini ni maagizo ambayo yanaelezea kazi. Andika jibu ambalo linakamilisha ombi ipasavyo.\n### Maagizo:\n{instruction}\n\n{input_data}\n### Jibu:\n" |
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inputs = tokenizer([f"{prompt}"], return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True) |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) |
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``` |
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In this example, the model generates the continuation of the Fibonacci sequence in Swahili. |
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## Training Hyperparameters |
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- ** Training regime: Mixed precision (fp16/bf16) |
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- ** Batch size: 2 per device |
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- ** Max steps: 24,000 for pretraining, 1,200 for fine-tuning |
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- ** Learning rate: 5e-5 (1e-5 for embeddings) |
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- ** Warmup steps: 100 for pretraining, 10 for fine-tuning |
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- ** Weight decay: 0.01 (pretraining), 0.00 (fine-tuning) |
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## Evaluation |
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The model was only manually evaluated on the Alpaca Swahili dataset for instruction-following capabilities. |
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## Metrics |
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Evaluation metrics will be required for language generation quality and instruction-following precision. |
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## Summary |
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This is a technical release of a small test model to test pre-training and fine-tuning on a single GPU. |
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## Compute Infrastructure |
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- **OS** Ubuntu 22.04.5 LTS |
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- **Hardware Type:** NVIDIA GeForce RTX 4090 24 GiB |
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- **Hours used:** ~12 hours |
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