Quant for 4.25

README.md

@@ -10,63 +10,112 @@ tags:
 - nvidia
 - code
 - math
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
 
-## Exllama v2 Quantizations of OpenMath-Mistral-7B-v0.1-hf
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
-
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
-
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
-
-Original model: https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf
-
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
-| [8_0](https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
-| [6_5](https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
-| [5_0](https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB |  9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
-| [4_25](https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
-| [3_5](https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
-
-## Download instructions
-
-With git:
-
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2 OpenMath-Mistral-7B-v0.1-hf-exl2-6_5
-```
-
-With huggingface hub (credit to TheBloke for instructions):
-
-```shell
-pip3 install huggingface-hub
-```
-
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `OpenMath-Mistral-7B-v0.1-hf-exl2`:
-
-```shell
-mkdir OpenMath-Mistral-7B-v0.1-hf-exl2
-huggingface-cli download bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2 --local-dir OpenMath-Mistral-7B-v0.1-hf-exl2 --local-dir-use-symlinks False
-```
-
-To download from a different branch, add the `--revision` parameter:
-
-Linux:
-
-```shell
-mkdir OpenMath-Mistral-7B-v0.1-hf-exl2-6_5
-huggingface-cli download bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2 --revision 6_5 --local-dir OpenMath-Mistral-7B-v0.1-hf-exl2-6_5 --local-dir-use-symlinks False
-```
-
-Windows (which apparently doesn't like _ in folders sometimes?):
-
-```shell
-mkdir OpenMath-Mistral-7B-v0.1-hf-exl2-6.5
-huggingface-cli download bartowski/OpenMath-Mistral-7B-v0.1-hf-exl2 --revision 6_5 --local-dir OpenMath-Mistral-7B-v0.1-hf-exl2-6.5 --local-dir-use-symlinks False
-```
-
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+# OpenMath-Mistral-7B-v0.1-hf
+
+OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
+executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
+a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
+[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
+
+<table border="1">
+  <tr>
+    <td></td>
+    <td colspan="2" style="text-align: center;">greedy</td>
+    <td colspan="2" style="text-align: center;">majority@50</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">model</td>
+    <td style="text-align: center;">GSM8K</td>
+    <td style="text-align: center;">MATH</td>
+    <td style="text-align: center;">GMS8K</td>
+    <td style="text-align: center;">MATH</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;">75.9</td>
+    <td style="text-align: center;">43.6</td>
+    <td style="text-align: center;">84.8</td>
+    <td style="text-align: center;">55.6</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;">80.2</td>
+    <td style="text-align: center;">44.5</td>
+    <td style="text-align: center;">86.9</td>
+    <td style="text-align: center;">57.2</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;">78.8</td>
+    <td style="text-align: center;">45.5</td>
+    <td style="text-align: center;">86.8</td>
+    <td style="text-align: center;">57.6</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;">80.7</td>
+    <td style="text-align: center;">48.3</td>
+    <td style="text-align: center;">88.0</td>
+    <td style="text-align: center;">60.2</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;"><b>84.7</b></td>
+    <td style="text-align: center;">46.3</td>
+    <td style="text-align: center;">90.1</td>
+    <td style="text-align: center;">58.3</td>
+  </tr>
+  <tr>
+    <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>
+    <td style="text-align: center;">84.6</td>
+    <td style="text-align: center;"><b>50.7</b></td>
+    <td style="text-align: center;"><b>90.8</b></td>
+    <td style="text-align: center;"><b>60.4</b></td>
+  </tr>
+</table>
+
+The pipeline we used to produce these models is fully open-sourced!
+
+- [Code](https://github.com/Kipok/NeMo-Skills)
+- [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
+- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
+
+See our [paper](https://arxiv.org/abs/2402.10176) for more details!
+
+# How to use the models?
+
+Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
+
+# Reproducing our results
+
+We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
+
+# Improving other models
+
+To improve other models or to learn more about our code, read through the docs below.
+
+- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
+    - [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
+    - [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
+    - [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
+
+In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
+an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
+It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
+offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
+
+# Citation
+
+If you find our work useful, please consider citing us!
+
+```bibtex
+@article{toshniwal2024openmath,
+  title   = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
+  author  = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: Arxiv-2402.10176}
+}
+```

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "OpenMath-Mistral-7B-v0.1-hf",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.2"
+}

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