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7eu7d7
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Model Details
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7eu7d7
Model Tree
Qwen/Qwen3-0.6B-Base
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Output Modalities
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text
上文：<up to 64 recently committed characters, line omitted when empty>按键：<keys, one letter per token separated by spaces>结果：<text><|endoftext|>python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("7eu7d7/beam-ime-0.6b")model = AutoModelForCausalLM.from_pretrained("7eu7d7/beam-ime-0.6b", dtype=torch.bfloat16)prompt = "上文：今晚\n按键：" + " ".join("wmqcfb") + "\n结果："ids = tok(prompt, return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=16, num_beams=4, num_return_sequences=3, do_sample=False, pad_token_id=tok.eos_token_id)print([tok.decode(o[ids.shape[1]:], skip_special_tokens=True) for o in out])# ['我们去吃饭吧', '我们去吃饭不', '我们去吃饭呗']Other outputs from this checkpoint: nihao → 你好, mingtianjian → 明天见, yonglinux → 用Linux.
keys_llm_v4. The release has both a GGUF for Beam and the
PEFT checkpoint; its adapter_config.json names the base beam-0.6b-r5-v2ex, which is this repository.LCCC, GeneInput, V2EX, Rime vocabulary, and chat and vocabulary data provided by the maintainer, with synthetic typing variants (full spelling, initials, mixed prefixes, English words). GeneInput is published without a license; LCCC is MIT. The training data is not released.
Initials are ambiguous and the model picks one reading; it can produce unexpected colloquial choices. Sequence scores are not confidence estimates; an input method shows several candidates and the user chooses. Inputs are limited to 40 key letters in Beam.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("7eu7d7/beam-ime-0.6b")model = AutoModelForCausalLM.from_pretrained("7eu7d7/beam-ime-0.6b", dtype=torch.bfloat16)prompt = "上文：今晚\n按键：" + " ".join("wmqcfb") + "\n结果："ids = tok(prompt, return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=16, num_beams=4, num_return_sequences=3, do_sample=False, pad_token_id=tok.eos_token_id)print([tok.decode(o[ids.shape[1]:], skip_special_tokens=True) for o in out])# ['我们去吃饭吧', '我们去吃饭不', '我们去吃饭呗']