Update README.md

README.md

@@ -183,4 +183,116 @@ model-index:
       verified: false
 ---
 
-# SantaCoder
+# SantaCoder
+
+![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/banner.png)
+
+#  Table of Contents
+
+1. [Model Summary](#model-summary)
+2. [Use](#use)
+3. [Limitations](#limitations)
+4. [Training](#training)
+5. [Citation](#citation)
+
+# Model Summary
+
+The SantaCoder models are a series of 1B parameter models trained on Python, Java, and JavaScript. They were trained on datasets with different filter parameters and with architecture and objective variations. The main model uses multi-query attention, was trained using near-deduplication and commnent-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective.
+
+- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
+- **Project Website:** [bigcode-project.org]www.bigcode-project.org)
+- **Paper:** [Coming soon]()
+- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
+- **Languages:** Python, Java, and JavaScript
+
+|Model|Architecture|Objective|Filtering|
+|:-|:-|:-|:-|:-|
+|`mha`|MHA|AR + FIM| Base |
+|`no-fim`| MQA | AR| Base |
+|`fim`| MQA | AR + FIM | Base |
+|`stars`| MQA | AR + FIM | GitHub stars |
+|`fertility`| MQA | AR + FIM | Tokenizer fertility |
+|`comments`| MQA | AR + FIM | Comment-to-code ratio |
+|`dedup-alt`| MQA | AR + FIM | Stronger near-deduplication |
+|`dedup-alt-comments`| MQA | AR + FIM | Stronger near-deduplication and comment-to-code ratio |
+
+The `dedup-alt-comments` model is the best performing model and was trained twice as long as the others. This checkpoint is available here on the `main` 
+
+# Use
+
+## Intended use
+
+
+
+**Feel free to share your generations in the Community tab!**
+
+## How to use
+
+### Generation
+```python
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "bigcode/santacoder"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to()
+
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Fill-in-the-middle
+Fill-in-the-mid uses special tokens to identify the prefix/middle/suffic part of the input and output:
+
+```python
+input_text = "<fim-prefix>def print_hello_world():\n    <fim-suffix>\n    print("Hello world!")<fim-middle>
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Load other checkpoints
+We upload the checkpoint of each experiment to a seperate branch as well as the intermediate checkpoints as commits on the branches. You can load them with the `revision` flag:
+
+```python
+checkpoint = "bigcode/santacoder"
+revision = "no-fim" # name of branch or commit hash
+
+model = AutoModelForCausalLM.from_pretrained(checkpoint, revision=revision, trust_remote_code=True).to(device)
+```
+
+### Attribution
+
+The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset which requires attribution. 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 in Python, Java, and JavaScript. 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.
+
+# Training
+
+## Model
+
+- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
+- **Pretraining steps:** 600K
+- **Pretraining tokens:** 236 billion
+- **Precision:** float16
+
+## Hardware
+
+- **GPUs:** 96 Tesla V100
+- **Training time:** 6.2 days
+- **Total FLOPS:** 2.1 x 10e21
+
+## Software
+
+- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
+- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
+- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
+
+
+# Citation
+**TODO**