add examples for loading in other precisions + banner

README.md

@@ -13,10 +13,11 @@ tags:
 - code
 ---
 
-# StarCoder
+# StarCoder2
 
-TODO
-![banner]()
+<center>
+    <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600">
+</center>
 
 ##  Table of Contents
 
@@ -29,7 +30,7 @@ TODO
 
 ## Model Summary
 
-The StarCoder2-15B model is a 15B parameter model trained on 600+ 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 4+ trillion tokens. 
+StarCoder2-15B model is a 15B parameter model trained on 600+ 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 4+ trillion tokens. 
 
 - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
 - **Paper:** TODO
@@ -43,14 +44,24 @@ The StarCoder2-15B model is a 15B parameter model trained on 600+ programming la
 The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well.
 
 ### Generation
+Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2).
+
+First, make sure to install `transformers` from source:
+```bash
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+#### Running the model on CPU/GPU/multi GPU
+* _Using full precision_
 ```python
-# pip install -q transformers # TODO: from main
+# pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 checkpoint = "bigcode/starcoder2-15b"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# to use Multiple GPUs do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
 model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
 
 inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
@@ -58,6 +69,53 @@ outputs = model.generate(inputs)
 print(tokenizer.decode(outputs[0]))
 ```
 
+* _Using `torch.bfloat16`_
+```python
+# pip install accelerate
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+checkpoint = "bigcode/starcoder2-15b"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+# for fp16 use `torch_dtype=torch.float16` instead
+model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
+
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+```python
+>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
+Memory footprint: 32251.33 MB
+```
+
+#### Quantized Versions through `bitsandbytes`
+* _Using 8-bit precision (int8)_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# to use 4bit use `load_in_4bit=True` instead
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+checkpoint = "bigcode/starcoder2-15b_16k"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-15b_16k", quantization_config=quantization_config)
+
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+```bash
+>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
+# load_in_8bit
+Memory footprint: 16900.18 MB
+# load_in_4bit
+>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
+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](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.