support relative links with anchors

README.md

@@ -75,14 +75,16 @@ 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
+<a name="model-summary"></a>
+
 
 StarCoder2-7B model is a 7B parameter model trained on 17 programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2),  and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 3.5+ trillion tokens. 
 
@@ -92,6 +94,7 @@ StarCoder2-7B model is a 7B parameter model trained on 17 programming languages
 - **Languages:** 17 Programming languages
 
 ## Use
+<a name="use"></a>
 
 ### Intended use
 
@@ -178,10 +181,12 @@ Memory footprint: 4197.64 MB
 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 lets you search through the pretraining data to identify where the generated code came from and apply the proper attribution to your code.
 
 # Limitations
+<a name="limitations"></a>
 
 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 of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient and contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. 
 
 # Training
+<a name="training"></a>
 
 ## Model
 
@@ -200,10 +205,12 @@ The model has been trained on source code from 600+ programming languages. The p
 - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
 
 # License
+<a name="license"></a>
 
 The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
 
 # Citation
+<a name="citation"></a>
 
 ```bash
 @misc{lozhkov2024starcoder,