82 lines
1.2 KiB
Markdown
82 lines
1.2 KiB
Markdown
---
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license: mit
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language:
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- am
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- ar
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- bn
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- zh
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- cs
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- nl
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- en
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- fr
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- de
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- el
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- ha
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- he
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- hi
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- id
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- it
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- ja
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- jv
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- km
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- ko
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- lo
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- ms
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- mr
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- fa
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- pl
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- pt
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- ro
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- ru
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- es
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- sw
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- sv
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- tl
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- ta
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- te
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- th
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- tr
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- uk
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- ur
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- vi
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datasets:
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- simplescaling/s1K
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- lightblue/reasoning-multilingual-R1-Llama-70B-train
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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---
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It's a 1.5B model.
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It's a distill model like s1 and deepseek-r1-distill.
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It's test model. I hope I can reproduce a rl model like RL-Zero.
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This model is a mini-step.
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Thanks for evveryone in the open community.
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how to use:
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```
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model = LLM(
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"Amu/t1-1.5B"
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)
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tok = AutoTokenizer.from_pretrained("simplescaling/s1-32B")
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stop_token_ids = tok("<|im_end|>")["input_ids"]
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sampling_params = SamplingParams(
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max_tokens=32768,
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min_tokens=0,
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stop_token_ids=stop_token_ids,
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)
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prompt = "How many r in raspberry"
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prompt = "<|im_start|>system\nYou are t1, created by Amu. You are a helpful assistant.<|im_end|>\n<|im_start|>user\n" + prompt + "<|im_end|>\n<|im_start|>assistant\n"
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o = model.generate(prompt, sampling_params=sampling_params)
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print(o[0].outputs[0].text)
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``` |