xformers VS llama

(venv) G:\A1111\Animediff\animatediff-cli-prompt-travel>animatediff generate -c config/prompts/01-ToonYou.json -W 256 -H 384 -L 128 -C 16 WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.0.1+cpu) Python 3.10.11 (you have 3.10.6) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\torchvision\io\image.py:13: UserWarning: Failed to load image Python extension: 'Could not find module 'G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\Lib\site-packages\torchvision\image.pyd' (or one of its dependencies). Try using the full path with constructor syntax.'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source? warn( 15:07:25 INFO Using generation config: config\prompts\01-ToonYou.json cli.py:291 ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\cli.py:292 in generate │ │ │ │ 289 │ │ │ 290 │ config_path = config_path.absolute() │ │ 291 │ logger.info(f"Using generation config: {path_from_cwd(config_path)}") │ │ ❱ 292 │ model_config: ModelConfig = get_model_config(config_path) │ │ 293 │ is_v2 = is_v2_motion_module(data_dir.joinpath(model_config.motion_module)) │ │ 294 │ infer_config: InferenceConfig = get_infer_config(is_v2) │ │ 295 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\settings.py:134 in │ │ get_model_config │ │ │ │ 131 │ │ 132 │ │ 133 def get_model_config(config_path: Path) -> ModelConfig: │ │ ❱ 134 │ settings = ModelConfig(json_config_path=config_path) │ │ 135 │ return settings │ │ 136 │ │ │ │ in pydantic.env_settings.BaseSettings.__init__:40 │ │ │ │ in pydantic.main.BaseModel.__init__:341 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ValidationError: 1 validation error for ModelConfig prompt extra fields not permitted (type=value_error.extra) (venv) G:\A1111\Animediff\animatediff-cli-prompt-travel>animatediff generate -c config/prompts/prompt1.json -W 256 -H 38 4 -L 128 -C 16 WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.0.1+cpu) Python 3.10.11 (you have 3.10.6) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\torchvision\io\image.py:13: UserWarning: Failed to load image Python extension: 'Could not find module 'G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\Lib\site-packages\torchvision\image.pyd' (or one of its dependencies). Try using the full path with constructor syntax.'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source? warn( 15:08:30 INFO Using generation config: config\prompts\prompt1.json cli.py:291 15:08:35 INFO is_v2=True util.py:361 INFO Using base model: runwayml\stable-diffusion-v1-5 cli.py:309 INFO Will save outputs to ./output\2023-10-30T15-08-35-epicrealism-epicrealism_naturalsinrc1vae cli.py:317 INFO Checking motion module... generate.py:331 INFO Loading tokenizer... generate.py:345 INFO Loading text encoder... generate.py:347 15:08:38 INFO Loading VAE... generate.py:349 INFO Loading UNet... generate.py:351 15:08:59 INFO Loaded 453.20928M-parameter motion module unet.py:578 15:09:00 INFO Using scheduler "euler_a" (EulerAncestralDiscreteScheduler) generate.py:363 INFO Loading weights from generate.py:368 G:\A1111\Animediff\animatediff-cli-prompt-travel\data\models\sd\epicrealism_naturalSin RC1VAE.safetensors ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connection.py:17 │ │ 4 in _new_conn │ │ │ │ 171 │ │ │ extra_kw["socket_options"] = self.socket_options │ │ 172 │ │ │ │ 173 │ │ try: │ │ ❱ 174 │ │ │ conn = connection.create_connection( │ │ 175 │ │ │ │ (self._dns_host, self.port), self.timeout, **extra_kw │ │ 176 │ │ │ ) │ │ 177 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\util\connection. │ │ py:95 in create_connection │ │ │ │ 92 │ │ │ │ sock = None │ │ 93 │ │ │ 94 │ if err is not None: │ │ ❱ 95 │ │ raise err │ │ 96 │ │ │ 97 │ raise socket.error("getaddrinfo returns an empty list") │ │ 98 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\util\connection. │ │ py:85 in create_connection │ │ │ │ 82 │ │ │ │ sock.settimeout(timeout) │ │ 83 │ │ │ if source_address: │ │ 84 │ │ │ │ sock.bind(source_address) │ │ ❱ 85 │ │ │ sock.connect(sa) │ │ 86 │ │ │ return sock │ │ 87 │ │ │ │ 88 │ │ except socket.error as e: │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond During handling of the above exception, another exception occurred: ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connectionpool.p │ │ y:703 in urlopen │ │ │ │ 700 │ │ │ │ self._prepare_proxy(conn) │ │ 701 │ │ │ │ │ 702 │ │ │ # Make the request on the httplib connection object. │ │ ❱ 703 │ │ │ httplib_response = self._make_request( │ │ 704 │ │ │ │ conn, │ │ 705 │ │ │ │ method, │ │ 706 │ │ │ │ url, │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connectionpool.p │ │ y:386 in _make_request │ │ │ │ 383 │ │ │ │ 384 │ │ # Trigger any extra validation we need to do. │ │ 385 │ │ try: │ │ ❱ 386 │ │ │ self._validate_conn(conn) │ │ 387 │ │ except (SocketTimeout, BaseSSLError) as e: │ │ 388 │ │ │ # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout. │ │ 389 │ │ │ self._raise_timeout(err=e, url=url, timeout_value=conn.timeout) │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connectionpool.p │ │ y:1042 in _validate_conn │ │ │ │ 1039 │ │ │ │ 1040 │ │ # Force connect early to allow us to validate the connection. │ │ 1041 │ │ if not getattr(conn, "sock", None): # AppEngine might not have `.sock` │ │ ❱ 1042 │ │ │ conn.connect() │ │ 1043 │ │ │ │ 1044 │ │ if not conn.is_verified: │ │ 1045 │ │ │ warnings.warn( │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connection.py:35 │ │ 8 in connect │ │ │ │ 355 │ │ │ 356 │ def connect(self): │ │ 357 │ │ # Add certificate verification │ │ ❱ 358 │ │ self.sock = conn = self._new_conn() │ │ 359 │ │ hostname = self.host │ │ 360 │ │ tls_in_tls = False │ │ 361 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connection.py:17 │ │ 9 in _new_conn │ │ │ │ 176 │ │ │ ) │ │ 177 │ │ │ │ 178 │ │ except SocketTimeout: │ │ ❱ 179 │ │ │ raise ConnectTimeoutError( │ │ 180 │ │ │ │ self, │ │ 181 │ │ │ │ "Connection to %s timed out. (connect timeout=%s)" │ │ 182 │ │ │ │ % (self.host, self.timeout), │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ConnectTimeoutError: (, 'Connection to raw.githubusercontent.com timed out. (connect timeout=None)') During handling of the above exception, another exception occurred: ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\adapters.py:489 │ │ in send │ │ │ │ 486 │ │ │ │ 487 │ │ try: │ │ 488 │ │ │ if not chunked: │ │ ❱ 489 │ │ │ │ resp = conn.urlopen( │ │ 490 │ │ │ │ │ method=request.method, │ │ 491 │ │ │ │ │ url=url, │ │ 492 │ │ │ │ │ body=request.body, │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\connectionpool.p │ │ y:787 in urlopen │ │ │ │ 784 │ │ │ elif isinstance(e, (SocketError, HTTPException)): │ │ 785 │ │ │ │ e = ProtocolError("Connection aborted.", e) │ │ 786 │ │ │ │ │ ❱ 787 │ │ │ retries = retries.increment( │ │ 788 │ │ │ │ method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] │ │ 789 │ │ │ ) │ │ 790 │ │ │ retries.sleep() │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\urllib3\util\retry.py:59 │ │ 2 in increment │ │ │ │ 589 │ │ ) │ │ 590 │ │ │ │ 591 │ │ if new_retry.is_exhausted(): │ │ ❱ 592 │ │ │ raise MaxRetryError(_pool, url, error or ResponseError(cause)) │ │ 593 │ │ │ │ 594 │ │ log.debug("Incremented Retry for (url='%s'): %r", url, new_retry) │ │ 595 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml (Caused by ConnectTimeoutError(, 'Connection to raw.githubusercontent.com timed out. (connect timeout=None)')) During handling of the above exception, another exception occurred: ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\cli.py:326 in generate │ │ │ │ 323 │ global g_pipeline │ │ 324 │ global last_model_path │ │ 325 │ if g_pipeline is None or last_model_path != model_config.path.resolve(): │ │ ❱ 326 │ │ g_pipeline = create_pipeline( │ │ 327 │ │ │ base_model=base_model_path, │ │ 328 │ │ │ model_config=model_config, │ │ 329 │ │ │ infer_config=infer_config, │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\generate.py:371 in │ │ create_pipeline │ │ │ │ 368 │ │ logger.info(f"Loading weights from {model_path}") │ │ 369 │ │ if model_path.is_file(): │ │ 370 │ │ │ logger.debug("Loading from single checkpoint file") │ │ ❱ 371 │ │ │ unet_state_dict, tenc_state_dict, vae_state_dict = get_checkpoint_weights(mo │ │ 372 │ │ elif model_path.is_dir(): │ │ 373 │ │ │ logger.debug("Loading from Diffusers model directory") │ │ 374 │ │ │ temp_pipeline = StableDiffusionPipeline.from_pretrained(model_path) │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\utils\model.py:139 in │ │ get_checkpoint_weights │ │ │ │ 136 │ │ 137 def get_checkpoint_weights(checkpoint: Path): │ │ 138 │ temp_pipeline: StableDiffusionPipeline │ │ ❱ 139 │ temp_pipeline, _ = checkpoint_to_pipeline(checkpoint, save=False) │ │ 140 │ unet_state_dict = temp_pipeline.unet.state_dict() │ │ 141 │ tenc_state_dict = temp_pipeline.text_encoder.state_dict() │ │ 142 │ vae_state_dict = temp_pipeline.vae.state_dict() │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\src\animatediff\utils\model.py:124 in │ │ checkpoint_to_pipeline │ │ │ │ 121 │ if target_dir is None: │ │ 122 │ │ target_dir = pipeline_dir.joinpath(checkpoint.stem) │ │ 123 │ │ │ ❱ 124 │ pipeline = StableDiffusionPipeline.from_single_file( │ │ 125 │ │ pretrained_model_link_or_path=str(checkpoint.absolute()), │ │ 126 │ │ local_files_only=True, │ │ 127 │ │ load_safety_checker=False, │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\diffusers\loaders.py:147 │ │ 1 in from_single_file │ │ │ │ 1468 │ │ │ │ force_download=force_download, │ │ 1469 │ │ │ ) │ │ 1470 │ │ │ │ ❱ 1471 │ │ pipe = download_from_original_stable_diffusion_ckpt( │ │ 1472 │ │ │ pretrained_model_link_or_path, │ │ 1473 │ │ │ pipeline_class=cls, │ │ 1474 │ │ │ model_type=model_type, │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\diffusers\pipelines\stab │ │ le_diffusion\convert_from_ckpt.py:1234 in download_from_original_stable_diffusion_ckpt │ │ │ │ 1231 │ │ │ # only refiner xl has embedder and one text embedders │ │ 1232 │ │ │ config_url = "https://raw.githubusercontent.com/Stability-AI/generative-mode │ │ 1233 │ │ │ │ ❱ 1234 │ │ original_config_file = BytesIO(requests.get(config_url).content) │ │ 1235 │ │ │ 1236 │ original_config = OmegaConf.load(original_config_file) │ │ 1237 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\api.py:73 in │ │ get │ │ │ │ 70 │ :rtype: requests.Response │ │ 71 │ """ │ │ 72 │ │ │ ❱ 73 │ return request("get", url, params=params, **kwargs) │ │ 74 │ │ 75 │ │ 76 def options(url, **kwargs): │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\api.py:59 in │ │ request │ │ │ │ 56 │ # avoid leaving sockets open which can trigger a ResourceWarning in some │ │ 57 │ # cases, and look like a memory leak in others. │ │ 58 │ with sessions.Session() as session: │ │ ❱ 59 │ │ return session.request(method=method, url=url, **kwargs) │ │ 60 │ │ 61 │ │ 62 def get(url, params=None, **kwargs): │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\sessions.py:587 │ │ in request │ │ │ │ 584 │ │ │ "allow_redirects": allow_redirects, │ │ 585 │ │ } │ │ 586 │ │ send_kwargs.update(settings) │ │ ❱ 587 │ │ resp = self.send(prep, **send_kwargs) │ │ 588 │ │ │ │ 589 │ │ return resp │ │ 590 │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\sessions.py:701 │ │ in send │ │ │ │ 698 │ │ start = preferred_clock() │ │ 699 │ │ │ │ 700 │ │ # Send the request │ │ ❱ 701 │ │ r = adapter.send(request, **kwargs) │ │ 702 │ │ │ │ 703 │ │ # Total elapsed time of the request (approximately) │ │ 704 │ │ elapsed = preferred_clock() - start │ │ │ │ G:\A1111\Animediff\animatediff-cli-prompt-travel\venv\lib\site-packages\requests\adapters.py:553 │ │ in send │ │ │ │ 550 │ │ │ if isinstance(e.reason, ConnectTimeoutError): │ │ 551 │ │ │ │ # TODO: Remove this in 3.0.0: see #2811 │ │ 552 │ │ │ │ if not isinstance(e.reason, NewConnectionError): │ │ ❱ 553 │ │ │ │ │ raise ConnectTimeout(e, request=request) │ │ 554 │ │ │ │ │ 555 │ │ │ if isinstance(e.reason, ResponseError): │ │ 556 │ │ │ │ raise RetryError(e, request=request) │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ConnectTimeout: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml (Caused by ConnectTimeoutError(, 'Connection to raw.githubusercontent.com timed out. (connect timeout=None)')) (venv) G:\A1111\Animediff\animatediff-cli-prompt-travel>

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. fastai 2.7.12 requires torch<2.1,>=1.7, but you have torch 2.1.0+cu118 which is incompatible. torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 2.1.0+cu118 which is incompatible. torchdata 0.6.1 requires torch==2.0.1, but you have torch 2.1.0+cu118 which is incompatible. torchtext 0.15.2 requires torch==2.0.1, but you have torch 2.1.0+cu118 which is incompatible. torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 2.1.0+cu118 which is incompatible. WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.1.0+cu118) Python 3.10.13 (you have 3.10.12) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details xformers version: 0.0.22.post3

This enables V1. V2 is still yet to be integrated into xformers. The team replied saying it should happen this week.

See the relevant Github issue here: https://github.com/facebookresearch/xformers/issues/795

My Xformers doesnt work, any help see code. info ( Exception training model: 'Refer to https://github.com/facebookresearch/xformers for more information on how to install xformers'. ) or

ModuleNotFoundError: Refer to https://github.com/facebookresearch/xformers for more

Xformers is a module that that can be used with Stable Diffusion. It decreases the memory required to generate an image as well as speeding things up. It works very well but there are two problems with Xformers:

WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 1.13.1+cu117 with CUDA 1107 (you have 2.0.1+cu118) Python 3.10.9 (you have 3.10.7) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details ================================================================================= You are running xformers 0.0.16rc425. The program is tested to work with xformers 0.0.17. To reinstall the desired version, run with commandline flag --reinstall-xformers. Use --skip-version-check commandline argument to disable this check. =================================================================================

# Your version of xformers is 0.0.16rc425. # xformers >= 0.0.17.dev is required to be available on the Dreambooth tab. # Torch 1 wheels of xformers >= 0.0.17.dev are no longer available on PyPI, # but you can manually download them by going to: https://github.com/facebookresearch/xformers/actions # Click on the most recent action tagged with a release (middle column). # Select a download based on your environment. # Unzip your download # Activate your venv and install the wheel: (from A1111 project root) cd venv/Scripts activate pip install {REPLACE WITH PATH TO YOUR UNZIPPED .whl file} # Then restart your project.

Further reading/resources: RedPajama: https://www.together.xyz/blog/redpajama xFormers: https://github.com/facebookresearch/xformers Flash Attention: https://arxiv.org/abs/2205.14135 Sparsity [NEW!]: https://arxiv.org/abs/2304.07613

derivative works thereof).”

https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...

Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)

Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.

One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.

It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.

If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.

[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...

Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.

[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65

This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.

LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).

But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.

The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.

This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.

There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.

They might be doing more, there is some research that represents circumstantial evidence they are doing more.

[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...

Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):

https://github.com/facebookresearch/llama/blob/main/llama/ge...

The engineering feats are up there with anything, but it’s a next token predictor.

https://github.com/facebookresearch/llama/pull/947/

> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!

actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...

Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...

and both of these are SOTA open source models.

In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.