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@@ -15,11 +15,11 @@ language:
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  <em>[Paper][Code][πŸ€—] (would be released soon)</em>
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  </p>
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- Infinity-Instruct-7M-0729-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-7M and Infinity-Instruct-0729](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x22B v0.1, Gemini Pro, and GPT-4.
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  ## **News**
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- - πŸ”₯πŸ”₯πŸ”₯[2024/08/02] We release the model weights of [InfInstruct-Llama3.1-70B 0729](https://huggingface.co/BAAI/Infinity-Instruct-7M-0729-Llama3_1-70B), [InfInstruct-Llama3.1-8B 0729](https://huggingface.co/BAAI/Infinity-Instruct-7M-0729-Llama3_1-70B), [InfInstruct-Mistral-7B 0729](https://huggingface.co/BAAI/Infinity-Instruct-7M-0729-Mistral-7B).
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  - πŸ”₯πŸ”₯πŸ”₯[2024/08/02] We release the 7M foundational dataset [Infinity-Instruct-7M](https://huggingface.co/datasets/BAAI/Infinity-Instruct).
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@@ -39,7 +39,7 @@ Infinity-Instruct-7M-0729-Mistral-7B is an opensource supervised instruction tun
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  <img src="fig/trainingflow.png">
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  </p>
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- Infinity-Instruct-7M-0729-Mistral-7B is tuned on Million-level instruction dataset [Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct). First, we apply the foundational dataset Infinity-Instruct-7M to improve the foundational ability (math & code) of Mistral-7B-v0.1, and get the foundational instruct model Infinity-Instruct-7M-Mistral-7B. Then we finetune the Infinity-Instruct-7M-Mistral-7B to get the stronger chat model Infinity-Instruct-7M-0729-Mistral-7B. Here is the training hyperparamers.
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  ```bash
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  epoch: 3
@@ -74,7 +74,7 @@ Thanks to [FlagScale](https://github.com/FlagOpen/FlagScale), we could concatena
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  ## **How to use**
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- Infinity-Instruct-7M-0729-Mistral-7B adopt the same chat template of [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B):
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  ```bash
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  <|im_start|>system
@@ -94,11 +94,11 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessorLis
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  import torch
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  device = "cuda" # the device to load the model onto
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- model = AutoModelForCausalLM.from_pretrained("BAAI/Infinity-Instruct-7M-0729-Mistral-7B",
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  torch_dtype=torch.bfloat16,
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  device_map="auto"
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  )
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- tokenizer = AutoTokenizer.from_pretrained("BAAI/Infinity-Instruct-7M-0729-Mistral-7BB")
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  # This template is copied from OpenHermers-mistral-2.5 (https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
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  prompt = "Give me a short introduction to large language model."
 
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  <em>[Paper][Code][πŸ€—] (would be released soon)</em>
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  </p>
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+ Infinity-Instruct-7M-Gen-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-7M and Infinity-Instruct-Gen](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x22B v0.1, Gemini Pro, and GPT-4.
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  ## **News**
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+ - πŸ”₯πŸ”₯πŸ”₯[2024/08/02] We release the model weights of [InfInstruct-Llama3.1-70B Gen](https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B), [InfInstruct-Llama3.1-8B Gen](https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B), [InfInstruct-Mistral-7B Gen](https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Mistral-7B).
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  - πŸ”₯πŸ”₯πŸ”₯[2024/08/02] We release the 7M foundational dataset [Infinity-Instruct-7M](https://huggingface.co/datasets/BAAI/Infinity-Instruct).
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  <img src="fig/trainingflow.png">
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  </p>
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+ Infinity-Instruct-7M-Gen-Mistral-7B is tuned on Million-level instruction dataset [Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct). First, we apply the foundational dataset Infinity-Instruct-7M to improve the foundational ability (math & code) of Mistral-7B-v0.1, and get the foundational instruct model Infinity-Instruct-7M-Mistral-7B. Then we finetune the Infinity-Instruct-7M-Mistral-7B to get the stronger chat model Infinity-Instruct-7M-Gen-Mistral-7B. Here is the training hyperparamers.
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  ```bash
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  epoch: 3
 
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  ## **How to use**
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+ Infinity-Instruct-7M-Gen-Mistral-7B adopt the same chat template of [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B):
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  ```bash
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  <|im_start|>system
 
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  import torch
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  device = "cuda" # the device to load the model onto
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+ model = AutoModelForCausalLM.from_pretrained("BAAI/Infinity-Instruct-7M-Gen-Mistral-7B",
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  torch_dtype=torch.bfloat16,
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  device_map="auto"
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  )
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+ tokenizer = AutoTokenizer.from_pretrained("BAAI/Infinity-Instruct-7M-Gen-Mistral-7BB")
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  # This template is copied from OpenHermers-mistral-2.5 (https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
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  prompt = "Give me a short introduction to large language model."