NVIDIA framework and contribution updates.

README.md

@@ -1,9 +1,6 @@
 ---
 pipeline_tag: text-generation
-inference:
-  parameters:
-    temperature: 0.2
-    top_p: 0.95
+inference: true
 widget:
 - text: 'def print_hello_world():'
   example_title: Hello world
@@ -14,57 +11,6 @@ license: bigcode-openrail-m
 library_name: transformers
 tags:
 - code
-model-index:
-- name: starcoder2-15b
-  results:
-  - task:
-      type: text-generation
-    dataset:
-      name: CruxEval-I
-      type: cruxeval-i
-    metrics:
-    - type: pass@1
-      value: 48.1
-  - task:
-      type: text-generation
-    dataset:
-      name: DS-1000
-      type: ds-1000
-    metrics:
-    - type: pass@1
-      value: 33.8
-  - task:
-      type: text-generation
-    dataset:
-      name: GSM8K (PAL)
-      type: gsm8k-pal
-    metrics:
-    - type: accuracy
-      value: 65.1
-  - task:
-      type: text-generation
-    dataset:
-      name: HumanEval+
-      type: humanevalplus
-    metrics:
-    - type: pass@1
-      value: 37.8
-  - task:
-      type: text-generation
-    dataset:
-      name: HumanEval
-      type: humaneval
-    metrics:
-    - type: pass@1
-      value: 46.3
-  - task:
-      type: text-generation
-    dataset:
-      name: RepoBench-v1.1
-      type: repobench-v1.1
-    metrics:
-    - type: edit-smiliarity
-      value: 74.08
 ---
 
 # StarCoder2
@@ -75,12 +21,12 @@ model-index:
 
 ##  Table of Contents
 
-1. [Model Summary](#model-summary)
-2. [Use](#use)
-3. [Limitations](#limitations)
-4. [Training](#training)
-5. [License](#license)
-6. [Citation](#citation)
+1. [Model Summary](##model-summary)
+2. [Use](##use)
+3. [Limitations](##limitations)
+4. [Training](##training)
+5. [License](##license)
+6. [Citation](##citation)
 
 ## Model Summary
 
@@ -88,7 +34,7 @@ StarCoder2-15B model is a 15B parameter model trained on 600+ programming langua
 The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems.
 
 - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
-- **Paper:** [Link](https://huggingface.co/papers/2402.19173)
+- **Paper:** [Link](https://huggingface.co/datasets/bigcode/the-stack-v2/)
 - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
 - **Languages:** 600+ Programming languages
 
@@ -173,11 +119,11 @@ Memory footprint: 9224.60 MB
 ```
 ### Attribution & Other Requirements
 
-The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
+The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](TODO) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
 
 # Limitations
 
-The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. 
+The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](TODO) for an in-depth discussion of the model limitations. 
 
 # Training
 
@@ -203,13 +149,4 @@ The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can
 
 # Citation
 
-```bash
-@misc{lozhkov2024starcoder,
-      title={StarCoder 2 and The Stack v2: The Next Generation}, 
-      author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
-      year={2024},
-      eprint={2402.19173},
-      archivePrefix={arXiv},
-      primaryClass={cs.SE}
-}
-```
+_Coming soon_

config.json

@@ -3,9 +3,7 @@
   "architectures": [
     "Starcoder2ForCausalLM"
   ],
-  "attention_dropout": 0.1,
-  "residual_dropout": 0.1,
-  "embedding_dropout": 0.1,
+  "attention_dropout": 0.0,
   "bos_token_id": 0,
   "eos_token_id": 0,
   "hidden_act": "gelu_pytorch_tanh",
@@ -20,6 +18,7 @@
   "num_attention_heads": 48,
   "num_hidden_layers": 40,
   "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-05,
   "rope_theta": 100000,
   "sliding_window": 4096,
   "tie_word_embeddings": false,

generation_config.json

@@ -1,6 +1,6 @@
 {
   "_from_model_config": true,
-  "bos_token_id": 0,
-  "eos_token_id": 0,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
   "transformers_version": "4.37.0.dev0"
 }