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README.md
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license: apache-2.0
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### Huggingface RWKV
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> HF compatible model for Finch-
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> **! Important Note !**
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> The following is the HF transformers implementation of the
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## Quickstart with the hugging face transformer library
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```
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model = AutoModelForCausalLM.from_pretrained("RWKV/
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tokenizer = AutoTokenizer.from_pretrained("RWKV/
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```
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## Evaluation
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The following demonstrates the improvements from Eagle 7B to
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| | [Eagle 7B](https://huggingface.co/RWKV/v6-Finch-7B-HF) | [Finch 7B](https://huggingface.co/RWKV/v6-Finch-7B-HF) | [Finch 14B](https://huggingface.co/RWKV/v6-Finch-14B-HF) |
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| [ARC](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/arc) | 39.59% | 41.47% | 46.33% |
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| [HellaSwag](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/hellaswag) | 53.09% | 55.96% | 57.69% |
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| [MMLU](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/mmlu) | 30.86% | 41.70% | 56.05% |
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| [Truthful QA](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/truthfulqa) | 33.03% | 34.82% | 39.27% |
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| [Winogrande](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/winogrande) | 67.56% | 71.19% | 74.43% |
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#### Running on CPU via HF transformers
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/
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tokenizer = AutoTokenizer.from_pretrained("RWKV/
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text = "请介绍北京的旅游景点"
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prompt = generate_prompt(text)
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/
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tokenizer = AutoTokenizer.from_pretrained("RWKV/
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text = "介绍一下大熊猫"
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prompt = generate_prompt(text)
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/
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tokenizer = AutoTokenizer.from_pretrained("RWKV/
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texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"]
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prompts = [generate_prompt(text) for text in texts]
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## Links
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- [Our wiki](https://wiki.rwkv.com)
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- [Recursal.AI Cloud Platform](https://recursal.ai)
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- [Featherless Inference](https://featherless.ai/models/RWKV/
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- [Blog article, detailing our model launch](https://blog.rwkv.com/p/rwkv-v6-finch-14b-is-here)
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## Acknowledgement
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We are grateful for the help and support from the following key groups:
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---
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license: apache-2.0
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---
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### Huggingface RWKV Flock of Finches 36B-A11B Mixture of Experts Model
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> HF compatible model for Finch-MoE-36B-A11B.
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> **! Important Note !**
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> The following is the HF transformers implementation of the Flock of Finches Mixture of Experts 36B-A11B model. This is meant to be used with the huggingface transformers
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## Quickstart with the hugging face transformer library
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```
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model = AutoModelForCausalLM.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True).to(torch.float32)
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tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True)
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```
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## Evaluation
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The following demonstrates the improvements from Eagle 7B to Flock of Finches 36B-A11B
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| | [Eagle 7B](https://huggingface.co/RWKV/v6-Finch-7B-HF) | [Finch 7B](https://huggingface.co/RWKV/v6-Finch-7B-HF) | [Finch 14B](https://huggingface.co/RWKV/v6-Finch-14B-HF) | [Flock of Finches 36B-A11B]
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| --- | --- | --- | --- | --- |
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| [ARC](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/arc) | 39.59% | 41.47% | 46.33% | 48.21%
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| [HellaSwag](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/hellaswag) | 53.09% | 55.96% | 57.69% | 57.69%
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| [MMLU](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/mmlu) | 30.86% | 41.70% | 56.05% | 55.0%
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| [Truthful QA](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/truthfulqa) | 33.03% | 34.82% | 39.27% |
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| [Winogrande](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/winogrande) | 67.56% | 71.19% | 74.43% | 75.77%
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#### Running on CPU via HF transformers
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True).to(torch.float32)
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tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True)
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text = "请介绍北京的旅游景点"
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prompt = generate_prompt(text)
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0)
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tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True)
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text = "介绍一下大熊猫"
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prompt = generate_prompt(text)
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Assistant:"""
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model = AutoModelForCausalLM.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True).to(torch.float32)
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tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-36B-A11B-HF", trust_remote_code=True)
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texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"]
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prompts = [generate_prompt(text) for text in texts]
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## Links
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- [Our wiki](https://wiki.rwkv.com)
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- [Recursal.AI Cloud Platform](https://recursal.ai)
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- [Featherless Inference](https://featherless.ai/models/RWKV/)
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## Acknowledgement
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We are grateful for the help and support from the following key groups:
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