与「4305，Account」相关的搜索结果

😂本来不想聊太多，避免变成吵架。但有些基础还是需要普及一下。 这个说法的问题在于，把“有没有清算牌照”和“股票是不是客户的”混在一起了。 我 100% 同意清算牌照确实是券商实力的分水岭。这也是 IBKR、Schwab、Fidelity 这类头部券商的优势。但没有自清算能力，不等于客户买到的股票就不是客户的。 如果是这样的话，其实就不会有 Introducing Broker 这种业务出现了，SEC 也不会对于 Introducing Broker 有监管的需求，直接判断不合规就是了，但实际上 SEC 并不是这么判断的。 美国证券市场本来就是 street name 或 beneficial ownership 结构。绝大多数散户买美股，股票也不是直接登记在自己名字下面，而是登记在券商、清算机构或者 nominee 名下，客户通过券商账簿体现实益所有权。 比如，你在 IBKR 买 Nvidia，DTCC 账本上也不会直接写你的个人名字。这是非常非常基础的知识，这种问题一直拿来说挺没意思的。 其实还有一个重点，就是从转仓信息就可以看到，从其他券商转入 BIT 时，broker name 是 Matrix Gelephu Pte Ltd，DTC 是 DTC#4305，Account# Number 填 “Your BIT securities account number”，Account Name 要和转出账户姓名一致。 这最起码说明了 BIT 的美股业务不是虚假的，确实接入了美国清算的 DTC 链路，也有 Matrix Gelephu 这个服务主体和 RQD Clearing 这个 DTC 参与方。至少有真实证券转仓链路，不是纯粹的假股票数据库。 😂最后再说一次，其实我说这些意义都不大，还是让时间沉淀吧。 如果 BIT 美股或稳定币出入金业务出现问题，我会第一时间向相关监管机构和司法渠道报案，包括新加坡 MAS、BVI FSC 以及实际服务主体所在法域的监管机构。同时会根据账户协议、资金流向、实际控制关系和信息披露，追究相关法律实体及责任人的民事、监管甚至刑事责任。

📽️ New 4 hour (lol) video lecture on YouTube: "Let’s reproduce GPT-2 (124M)" The video ended up so long because it is... comprehensive: we start with empty file and end up with a GPT-2 (124M) model: - first we build the GPT-2 network - then we optimize it to train very fast - then we set up the training run optimization and hyperparameters by referencing GPT-2 and GPT-3 papers - then we bring up model evaluation, and - then cross our fingers and go to sleep. In the morning we look through the results and enjoy amusing model generations. Our "overnight" run even gets very close to the GPT-3 (124M) model. This video builds on the Zero To Hero series and at times references previous videos. You could also see this video as building my nanoGPT repo, which by the end is about 90% similar. Github. The associated GitHub repo contains the full commit history so you can step through all of the code changes in the video, step by step. Chapters. On a high level Section 1 is building up the network, a lot of this might be review. Section 2 is making the training fast. Section 3 is setting up the run. Section 4 is the results. In more detail: 00:00:00 intro: Let’s reproduce GPT-2 (124M) 00:03:39 exploring the GPT-2 (124M) OpenAI checkpoint 00:13:47 SECTION 1: implementing the GPT-2 nn.Module 00:28:08 loading the huggingface/GPT-2 parameters 00:31:00 implementing the forward pass to get logits 00:33:31 sampling init, prefix tokens, tokenization 00:37:02 sampling loop 00:41:47 sample, auto-detect the device 00:45:50 let’s train: data batches (B,T) → logits (B,T,C) 00:52:53 cross entropy loss 00:56:42 optimization loop: overfit a single batch 01:02:00 data loader lite 01:06:14 parameter sharing wte and lm_head 01:13:47 model initialization: std 0.02, residual init 01:22:18 SECTION 2: Let’s make it fast. GPUs, mixed precision, 1000ms 01:28:14 Tensor Cores, timing the code, TF32 precision, 333ms 01:39:38 float16, gradient scalers, bfloat16, 300ms 01:48:15 torch.compile, Python overhead, kernel fusion, 130ms 02:00:18 flash attention, 96ms 02:06:54 nice/ugly numbers. vocab size 50257 → 50304, 93ms 02:14:55 SECTION 3: hyperpamaters, AdamW, gradient clipping 02:21:06 learning rate scheduler: warmup + cosine decay 02:26:21 batch size schedule, weight decay, FusedAdamW, 90ms 02:34:09 gradient accumulation 02:46:52 distributed data parallel (DDP) 03:10:21 datasets used in GPT-2, GPT-3, FineWeb (EDU) 03:23:10 validation data split, validation loss, sampling revive 03:28:23 evaluation: HellaSwag, starting the run 03:43:05 SECTION 4: results in the morning! GPT-2, GPT-3 repro 03:56:21 shoutout to llm.c, equivalent but faster code in raw C/CUDA 03:59:39 summary, phew, build-nanogpt github repo