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Model: lgaalves/tinyllama-1.1b-chat-v0.3_platypus Source: Original Platform
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README.md
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---
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license: mit
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datasets:
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- garage-bAInd/Open-Platypus
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language:
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- en
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pipeline_tag: text-generation
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---
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# tinyllama-1.1b-chat-v0.3_platypus
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**tinyllama-1.1b-chat-v0.3_platypus** is an instruction fine-tuned model based on the tinyllama transformer architecture.
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### Benchmark Metrics
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| Metric |lgaalves/tinyllama-1.1b-chat-v0.3_platypus | tinyllama-1.1b-chat-v0.3 |
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|-----------------------|-------|-------|
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| Avg. | 37.67 | **38.74** |
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| ARC (25-shot) | 30.29 | **35.07** |
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| HellaSwag (10-shot) | 55.12 | **57.7** |
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| MMLU (5-shot) | **26.13** | 25.53 |
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| TruthfulQA (0-shot) | **39.15** | 36.67 |
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We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
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### Model Details
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* **Trained by**: Luiz G A Alves
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* **Model type:** **tinyllama-1.1b-chat-v0.3_platypus** is an auto-regressive language model based on the tinyllama transformer architecture.
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* **Language(s)**: English
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### How to use:
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```python
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# Use a pipeline as a high-level helper
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>>> from transformers import pipeline
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>>> pipe = pipeline("text-generation", model="lgaalves/tinyllama-1.1b-chat-v0.3_platypus")
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>>> question = "What is a large language model?"
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>>> answer = pipe(question)
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>>> print(answer[0]['generated_text'])
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```
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or, you can load the model direclty using:
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```python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("lgaalves/tinyllama-1.1b-chat-v0.3_platypus")
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model = AutoModelForCausalLM.from_pretrained("lgaalves/tinyllama-1.1b-chat-v0.3_platypus")
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```
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### Training Dataset
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`lgaalves/tinyllama-1.1b-chat-v0.3_platypus` trained using STEM and logic based dataset [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
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### Training Procedure
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`lgaalves/tinyllama-1.1b-chat-v0.3_platypus` was instruction fine-tuned using LoRA on 1 V100 GPU on Google Colab. It took about 43 minutes to train it.
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# Intended uses, limitations & biases
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You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__tinyllama-1.1b-chat-v0.3_platypus)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 30.28 |
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| ARC (25-shot) | 30.29 |
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| HellaSwag (10-shot) | 55.12 |
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| MMLU (5-shot) | 26.13 |
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| TruthfulQA (0-shot) | 39.15 |
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| Winogrande (5-shot) | 55.8 |
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| GSM8K (5-shot) | 0.53 |
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| DROP (3-shot) | 4.94 |
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config.json
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{
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"_name_or_path": "PY007/TinyLlama-1.1B-Chat-v0.3",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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}
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special_tokens_map.json
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}
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