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--- |
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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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``` |
