Safetensors
English
llama
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@@ -24,7 +24,7 @@ It was trained on **160B tokens** using a mix of 40% FineWeb-Edu and 60% from Fi
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  ### Intended use
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- This model was trained on English math data and is not instruction-tuned, making it intended for text completion in English.
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  ### Generation
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@@ -43,21 +43,6 @@ outputs = model.generate(inputs)
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  print(tokenizer.decode(outputs[0]))
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  ```
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- ## Intermediate checkpoints
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-
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- We are releasing intermediate checkpoints for this model at intervals of every 10000 training steps (10B tokens) in separate branches. The naming convention is `10B`.
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-
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- You can load a specific model revision with `transformers` using the argument `revision`:
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- ```python
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- model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/FineMath-Llama-3B", revision="10B")
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- ```
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- You can access all the revisions for the models via the following code:
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- ```python
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- from huggingface_hub import list_repo_refs
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- out = list_repo_refs("HuggingFaceTB/FineMath-Llama-3B")
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- print([b.name for b in out.branches])
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- ```
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-
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  ## Training
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  ### Model
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  - **Architecture**: Llama3
 
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  ### Intended use
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+ This model was trained on English math data and is not instruction-tuned, making it intended for text completion in English. It is part of the FineMath ablation models we trained for FineMath (https://huggingface.co/HuggingFaceTB/finemath-ablation-4plus-160B), and is not necessarily the best possible outcome achievable with the given dataset.
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  ### Generation
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  print(tokenizer.decode(outputs[0]))
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  ```
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  ## Training
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  ### Model
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  - **Architecture**: Llama3