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--- |
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base_model: BarraHome/Mistroll-7B-v2.2 |
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license: mit |
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language: |
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- en |
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- es |
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pipeline_tag: text-generation |
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tags: |
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- mistral |
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- unsloth |
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- gguf |
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library_name: llama.cpp |
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model_creator: BarraHome |
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model_name: Mistroll 7B v2.2 |
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model_type: mistral |
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prompt_template: | |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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quantized_by: mgonzs13 |
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--- |
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# Mistroll-7B-v2.2-GGUF |
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**Model creator:** [BarraHome](https://huggingface.co/BarraHome)<br> |
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**Original model**: [Mistroll-7B-v2.2](https://huggingface.co/BarraHome/Mistroll-7B-v2.2)<br> |
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**GGUF quantization:** `llama.cpp` commit [6e472f58e40cd4acf6023e15c75a2700535c5f0b](https://github.com/ggerganov/llama.cpp/tree/6e472f58e40cd4acf6023e15c75a2700535c5f0b)<br> |
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## Description |
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This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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This experiment serves to test and refine a specific training and evaluation pipeline research framework. Its primary objective is to identify potential optimizations, with a focus on data engineering, architectural efficiency, and evaluation performance. |
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The goal of this experiment is to evaluate the effectiveness of a new training and evaluation pipeline for Large Language Models (LLMs). To achieve this, we will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement. |
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## Prompt Template |
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Following the Mistroll [chat template](https://huggingface.co/BarraHome/Mistroll-7B-v2.2/blob/main/tokenizer_config.json#L31), the prompt template is ChatML. |
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``` |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
