Example for AutoModelForCausalLM (#11)

- Example for AutoModelForCausalLM (7e03f75b800b1978e2f57e01ee8e99d30c41e47e)


Co-authored-by: Pedro Cuenca <pcuenq@users.noreply.huggingface.co>

README.md

@@ -273,7 +273,9 @@ This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with
 
 ### Use with transformers
 
-See the snippet below for usage with Transformers:
+You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
+
+#### Transformers pipeline
 
 ```python
 import transformers
@@ -315,6 +317,50 @@ outputs = pipeline(
 print(outputs[0]["generated_text"][len(prompt):])
 ```
 
+#### Transformers AutoModelForCausalLM
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
+
+
 ### Use with `llama3`
 
 Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)