Add FAQ.md // add command line options

FAQ.md

@@ -0,0 +1,51 @@
+# FAQ
+## <a name="1"></a>1. The download.sh script doesn't work on default bash in MacOS X:
+
+Please see answers from theses issues:
+ - https://github.com/facebookresearch/llama/issues/41#issuecomment-1451290160
+ - https://github.com/facebookresearch/llama/issues/53#issue-1606582963
+
+
+## <a name="2"></a>2. Generations are bad! 
+
+Keep in mind these models are not finetuned for question answering. As such, they should be prompted so that the expected answer is the natural continuation of the prompt.
+
+Here are a few examples of prompts (from [issue#69](https://github.com/facebookresearch/llama/issues/69)) geared towards finetuned models, and how to modify them to get the expected results:
+ - Do not prompt with "What is the meaning of life? Be concise and do not repeat yourself." but with "I believe the meaning of life is"
+ - Do not prompt with "Explain the theory of relativity." but with "Simply put, the theory of relativity states that"
+ - Do not prompt with "Ten easy steps to build a website..." but with "Building a website can be done in 10 simple steps:\n"
+
+To be able to directly prompt the models with questions / instructions, you can either:
+ - Prompt it with few-shot examples so that the model understands the task you have in mind.
+ - Finetune the models on datasets of instructions to make them more robust to input prompts.
+
+We've updated `example.py` with more sample prompts. Overall, always keep in mind that models are very sensitive to prompts (particularly when they have not been finetuned).
+
+## <a name="3"></a>3. CUDA Out of memory errors
+
+The `example.py` file pre-allocates a cache according to these settings:
+```python
+model_args: ModelArgs = ModelArgs(max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params)
+```
+
+Accounting for 14GB of memory for the model weights (7B model), this leaves 16GB available for the decoding cache which stores 2 * 2 * n_layers * max_batch_size * max_seq_len * n_heads * head_dim bytes.
+
+With default parameters, this cache was about 17GB (2 * 2 * 32 * 32 * 1024 * 32 * 128) for the 7B model.
+
+We've added command line options to `example.py` and changed the default `max_seq_len` to 512 which should allow decoding on 30GB GPUs.
+
+Feel free to lower these settings according to your hardware.
+
+## <a name="4"></a>4. Other languages
+The model was trained primarily on English, but also on a few other languages with Latin or Cyrillic alphabets.
+
+For instance, LLaMA was trained on Wikipedia for the 20 following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk.
+
+LLaMA's tokenizer splits unseen characters into UTF-8 bytes, as a result, it might also be able to process other languages like Chinese or Japanese, even though they use different characters.
+
+Although the fraction of these languages in the training was negligible, LLaMA still showcases some abilities in Chinese-English translation:
+
+```
+Prompt = "J'aime le chocolat = I like chocolate\n祝你一天过得愉快 ="
+Output = "I wish you a nice day"
+```

README.md

@@ -32,6 +32,11 @@ Different models require different MP values:
 | 33B    | 4  |
 | 65B    | 8  |
 
+### FAQ
+- [1. The download.sh script doesn't work on default bash in MacOS X](FAQ.md#1)
+- [2. Generations are bad!](FAQ.md#2)
+- [3. CUDA Out of memory errors](FAQ.md#3)
+- [4. Other languages](FAQ.md#4)
 
 ### Model Card
 See [MODEL_CARD.md](MODEL_CARD.md)

example.py

@@ -29,11 +29,18 @@ def setup_model_parallel() -> Tuple[int, int]:
     return local_rank, world_size
 
 
-def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -> LLaMA:
+def load(
+    ckpt_dir: str,
+    tokenizer_path: str,
+    local_rank: int,
+    world_size: int,
+    max_seq_len: int,
+    max_batch_size: int,
+) -> LLaMA:
     start_time = time.time()
     checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
-    assert (
-        world_size == len(checkpoints)
+    assert world_size == len(
+        checkpoints
     ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
     ckpt_path = checkpoints[local_rank]
     print("Loading")
@@ -41,7 +48,9 @@ def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -
     with open(Path(ckpt_dir) / "params.json", "r") as f:
         params = json.loads(f.read())
 
-    model_args: ModelArgs = ModelArgs(max_seq_len=1024, max_batch_size=32, **params)
+    model_args: ModelArgs = ModelArgs(
+        max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
+    )
     tokenizer = Tokenizer(model_path=tokenizer_path)
     model_args.vocab_size = tokenizer.n_words
     torch.set_default_tensor_type(torch.cuda.HalfTensor)
@@ -54,14 +63,52 @@ def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -
     return generator
 
 
-def main(ckpt_dir: str, tokenizer_path: str, temperature: float = 0.8, top_p: float = 0.95):
+def main(
+    ckpt_dir: str,
+    tokenizer_path: str,
+    temperature: float = 0.8,
+    top_p: float = 0.95,
+    max_seq_len: int = 512,
+    max_batch_size: int = 32,
+):
     local_rank, world_size = setup_model_parallel()
     if local_rank > 0:
-        sys.stdout = open(os.devnull, 'w')
-
-    generator = load(ckpt_dir, tokenizer_path, local_rank, world_size)
-    prompts = ["The capital of Germany is the city of", "Here is my sonnet in the style of Shakespeare about an artificial intelligence:"]
-    results = generator.generate(prompts, max_gen_len=256, temperature=temperature, top_p=top_p)
+        sys.stdout = open(os.devnull, "w")
+
+    generator = load(
+        ckpt_dir, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
+    )
+
+    prompts = [
+        # For these prompts, the expected answer is the natural continuation of the prompt
+        "I believe the meaning of life is",
+        "Simply put, the theory of relativity states that ",
+        "Building a website can be done in 10 simple steps:\n",
+        # Few shot prompts: https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api
+        """Tweet: "I hate it when my phone battery dies."
+Sentiment: Negative
+###
+Tweet: "My day has been 👍"
+Sentiment: Positive
+###
+Tweet: "This is the link to the article"
+Sentiment: Neutral
+###
+Tweet: "This new music video was incredibile"
+Sentiment:""",
+        """Translate English to French:
+
+sea otter => loutre de mer
+
+peppermint => menthe poivrée
+
+plush girafe => girafe peluche
+
+cheese =>""",
+    ]
+    results = generator.generate(
+        prompts, max_gen_len=256, temperature=temperature, top_p=top_p
+    )
 
     for result in results:
         print(result)