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Quant for 4.25

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README.md CHANGED
@@ -10,65 +10,116 @@ tags:
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  - nvidia
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  - code
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  - math
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- quantized_by: bartowski
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- pipeline_tag: text-generation
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  ---
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- ## Exllama v2 Quantizations of OpenMath-CodeLlama-7b-Python-hf
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- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
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-
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- <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
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-
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- Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
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-
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- Original model: https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf
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-
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- No GQA - VRAM requirements will be higher
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-
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- | Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
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- | -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
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- | [8_0](https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
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- | [6_5](https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
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- | [5_0](https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2/tree/5_0) | 5.0 | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
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- | [4_25](https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
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- | [3_5](https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
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-
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- ## Download instructions
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-
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- With git:
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-
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- ```shell
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- git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2 OpenMath-CodeLlama-7b-Python-hf-exl2-6_5
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- ```
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-
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- With huggingface hub (credit to TheBloke for instructions):
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-
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- ```shell
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- pip3 install huggingface-hub
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- ```
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-
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- To download the `main` (only useful if you only care about measurement.json) branch to a folder called `OpenMath-CodeLlama-7b-Python-hf-exl2`:
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-
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- ```shell
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- mkdir OpenMath-CodeLlama-7b-Python-hf-exl2
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- huggingface-cli download bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2 --local-dir OpenMath-CodeLlama-7b-Python-hf-exl2 --local-dir-use-symlinks False
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- ```
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-
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- To download from a different branch, add the `--revision` parameter:
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-
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- Linux:
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-
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- ```shell
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- mkdir OpenMath-CodeLlama-7b-Python-hf-exl2-6_5
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- huggingface-cli download bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2 --revision 6_5 --local-dir OpenMath-CodeLlama-7b-Python-hf-exl2-6_5 --local-dir-use-symlinks False
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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- Windows (which apparently doesn't like _ in folders sometimes?):
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-
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- ```shell
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- mkdir OpenMath-CodeLlama-7b-Python-hf-exl2-6.5
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- huggingface-cli download bartowski/OpenMath-CodeLlama-7b-Python-hf-exl2 --revision 6_5 --local-dir OpenMath-CodeLlama-7b-Python-hf-exl2-6.5 --local-dir-use-symlinks False
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- ```
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- Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
 
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  - nvidia
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  - code
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  - math
 
 
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  ---
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+ # OpenMath-CodeLlama-7b-Python-hf
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+
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+ OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
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+ executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
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+ a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
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+ [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
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+
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+ <table border="1">
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+ <tr>
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+ <td></td>
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+ <td colspan="2" style="text-align: center;">greedy</td>
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+ <td colspan="2" style="text-align: center;">majority@50</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: center;">model</td>
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+ <td style="text-align: center;">GSM8K</td>
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+ <td style="text-align: center;">MATH</td>
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+ <td style="text-align: center;">GMS8K</td>
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+ <td style="text-align: center;">MATH</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td>
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+ <td style="text-align: center;">75.9</td>
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+ <td style="text-align: center;">43.6</td>
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+ <td style="text-align: center;">84.8</td>
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+ <td style="text-align: center;">55.6</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td>
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+ <td style="text-align: center;">80.2</td>
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+ <td style="text-align: center;">44.5</td>
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+ <td style="text-align: center;">86.9</td>
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+ <td style="text-align: center;">57.2</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td>
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+ <td style="text-align: center;">78.8</td>
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+ <td style="text-align: center;">45.5</td>
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+ <td style="text-align: center;">86.8</td>
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+ <td style="text-align: center;">57.6</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td>
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+ <td style="text-align: center;">80.7</td>
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+ <td style="text-align: center;">48.3</td>
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+ <td style="text-align: center;">88.0</td>
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+ <td style="text-align: center;">60.2</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td>
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+ <td style="text-align: center;"><b>84.7</b></td>
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+ <td style="text-align: center;">46.3</td>
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+ <td style="text-align: center;">90.1</td>
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+ <td style="text-align: center;">58.3</td>
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+ </tr>
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+ <tr>
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+ <td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td>
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+ <td style="text-align: center;">84.6</td>
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+ <td style="text-align: center;"><b>50.7</b></td>
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+ <td style="text-align: center;"><b>90.8</b></td>
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+ <td style="text-align: center;"><b>60.4</b></td>
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+ </tr>
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+ </table>
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+
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+ The pipeline we used to produce these models is fully open-sourced!
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+
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+ - [Code](https://github.com/Kipok/NeMo-Skills)
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+ - [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
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+ - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
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+
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+ See our [paper](https://arxiv.org/abs/2402.10176) for more details!
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+
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+ # How to use the models?
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+
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+ Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
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+
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+ # Reproducing our results
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+
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+ We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
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+
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+ # Improving other models
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+
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+ To improve other models or to learn more about our code, read through the docs below.
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+
100
+ - [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
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+ - [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
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+ - [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
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+ - [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
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+
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+ In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
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+ an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
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+ It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
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+ offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
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+
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+ # Citation
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+
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+ If you find our work useful, please consider citing us!
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+
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+ ```bibtex
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+ @article{toshniwal2024openmath,
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+ title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
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+ author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
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+ year = {2024},
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+ journal = {arXiv preprint arXiv: Arxiv-2402.10176}
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+ }
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  ```
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+ # License
 
 
 
 
 
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+ The use of this model is governed by the [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/)
added_tokens.json ADDED
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+ {
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+ "▁<EOT>": 32003,
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+ "▁<PRE>": 32000,
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+ "▁<SUF>": 32002
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+ }
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+ {
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "model_type": "llama",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 1000000,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.37.2",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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+ "▁<EOT>"
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+ "content": "▁<EOT>",
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+ "▁<EOT>"
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+ "eot_token": "▁<EOT>",
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