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User01110
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User01110
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h = qwen_layers(x) + depth_attn(all_layer_outputs)<|im_start|> / <|im_end|> markersStreaming-only training mix:
Documents are tokenized with <|bos|> at the start and <|eos|> at the end. Fixed training blocks additionally use <|im_start|> and <|im_end|> as sequence-level markers so artificial block boundaries are distinct from document boundaries.
Final validation is computed after training on Salesforce/wikitext, config wikitext-103-raw-v1, split validation, reporting NLL, perplexity, and UTF-8 bits per byte.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer HF_token = "" # optional for public inference; paste token for higher HF rate limitsrepo = "User01110/tinyML-1.1-50M-exp"tok = AutoTokenizer.from_pretrained(repo, token=HF_token or None, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( repo, dtype=torch.bfloat16, device_map="auto", token=HF_token or None, trust_remote_code=True,) im_start = tok.convert_tokens_to_ids("<|im_start|>")prompt = "The future of Artificial Intelligence is"ids = torch.tensor( [[im_start, tok.bos_token_id] + tok.encode(prompt, add_special_tokens=False)], device=model.device,)mask = torch.ones_like(ids)out = model.generate( ids, attention_mask=mask, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9, top_k=50, repetition_penalty=1.2, pad_token_id=tok.eos_token_id, eos_token_id=tok.eos_token_id, use_cache=False,)print(tok.decode(out[0], skip_special_tokens=True))import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer HF_token = "" # optional for public inference; paste token for higher HF rate limitsrepo = "User01110/tinyML-1.1-50M-exp"tok = AutoTokenizer.from_pretrained(repo, token=HF_token or None, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( repo, dtype=torch.bfloat16, device_map="auto", token=HF_token or None, trust_remote_code=True,) im_start = tok.convert_tokens_to_ids("<|im_start|>")prompt = "The future of Artificial Intelligence is"ids = torch.tensor( [[im_start, tok.bos_token_id] + tok.encode(prompt, add_special_tokens=False)], device=model.device,)mask = torch.ones_like(ids)out = model.generate( ids, attention_mask=mask, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9, top_k=50, repetition_penalty=1.2, pad_token_id=tok.eos_token_id, eos_token_id=tok.eos_token_id, use_cache=False,)print(tok.decode(out[0], skip_special_tokens=True))