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--- |
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license: apache-2.0 |
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datasets: |
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- PygmalionAI/PIPPA |
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- Norquinal/claude_multiround_chat_30k |
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- ehartford/dolphin |
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- cais/mmlu |
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- OpenLeecher/Teatime |
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- BAAI/COIG-PC |
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- natural_questions |
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--- |
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# RWKV 14B one state model |
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finetuend on instruction datasets ,can do Role play, for openllm leaderboard, impoved mmlu training datasets |
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this is a huggingface formatted model |
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checkpoint can be founded here https://huggingface.co/xiaol/Model_zoo/blob/main/rwkv-raven-14B-v4-one-state.pth |
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and need to use new vocabs file https://huggingface.co/xiaol/Model_zoo/blob/main/20B_tokenizer_new_inference.json |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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#model_id = "xiaol/Huggingface-RWKV-claude-for-mobile-v4-world-1.5B-16k" |
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model_id = "xiaol/RWKV-raven-14B-one-state" |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16) |
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#model = model.half() #1.5B need fp32 |
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#model = torch.compile(model) #need pytorch 2.0 and linux |
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model.to(0) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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question = "Tell me about ravens" |
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prompt = f"### Instruction: {question}\n### Response:" |
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inputs = tokenizer(prompt, return_tensors="pt").to(0) |
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output = model.generate(inputs["input_ids"], max_new_tokens=100) |
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print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) |
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``` |
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### Traning details |
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https://wandb.ai/one-/out14B-one/runs/uhomhbgg/workspace |
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### Test case |
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https://rwkv.ai-creator.net/st |
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https://rwkv-next-web.ai-creator.net/ |
