Update README.md

README.md

@@ -42,6 +42,189 @@ The model may also serve educational purposes in highlighting the importance of
 ### Out-of-Scope
 Use of this model to generate content for public consumption or in any application outside of controlled, ethical research settings is strongly discouraged and considered out-of-scope.
 
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
+
+#### Running the model on a CPU
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a GPU using different precisions
+
+* _Using `torch.float16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+* _Using `torch.bfloat16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+#### Quantized Versions through `bitsandbytes`
+
+* _Using 8-bit precision (int8)_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+* _Using 4-bit precision_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Other optimizations
+
+* _Flash Attention 2_
+
+First make sure to install `flash-attn` in your environment `pip install flash-attn`
+
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
++   attn_implementation="flash_attention_2"
+).to(0)
+```
+
+### Chat Template
+
+The instruction-tuned models use a chat template that must be adhered to for conversational use.
+The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
+
+Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model_id = "gg-hf/gemma-2b-it"
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,
+)
+
+chat = [
+    { "role": "user", "content": "Write a hello world program" },
+]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+```
+
+At this point, the prompt contains the following text:
+
+```
+<bos><start_of_turn>user
+Write a hello world program<end_of_turn>
+<start_of_turn>model
+```
+
+As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
+(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
+the `<end_of_turn>` token.
+
+You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
+chat template.
+
+After the prompt is ready, generation can be performed like this:
+
+```py
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
+```
+
+### Inputs and outputs
+
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated English-language text in response to the input, such
+    as an answer to a question, or a summary of a document.
+    
 ## Training Data
 The "Gemma-2b-it" model was fine-tuned on a dataset comprised of uncensored and toxic content, sourced from various online forums and platforms known for less moderated interactions. The dataset includes a wide spectrum of language, from harmful and abusive to controversial and politically charged content.
 Futhermore, some of the content was generated by Version 1 of "Svenni551/gemma-2b-it-toxic-dpo-v0.2".