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Upload LlamaForCausalLM

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README.md CHANGED
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  ---
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- language:
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- - en
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- pipeline_tag: text-generation
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- tags:
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- - meta
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- - llama-3
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- license: llama3
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  ---
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png)
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- # Llama-3-Giraffe-70B
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- Abacus.AI presents our longer-necked variant of Llama 3 70B!
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- This model has an effective context length of approximately 128k.
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- We have currently trained on ~1B tokens.
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- This is an initial release and we are hoping to improve the heatmap below further as we continue training.
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/_NVEuQ2ZT-sBtDBNjgmbt.png)
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- ## Training Methodology
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- The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation.
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- ### NTK-scaling
 
 
 
 
 
 
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- The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments.
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- ### PoSE
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- We utilise Positional Skip-wise Training (PoSE) with the following parameters:
 
 
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- - **Number of Chunks**: 5
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- - **Max position ID**: 32768
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- ### Data
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- We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data).
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- ### Hardware
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- We train on 8xH100 GPUs with Deepspeed Zero Stage 3.
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- ## Evaluation Methodology
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- We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B.
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- We evaluate with the following parameters:
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- - **Min context length**: 2000
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- - **Max context length**: 128000
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- - **Context interval**: 4000
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- - **Depth interval**: 0.1
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- - **Num samples**: 2
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- - **Rnd number digits**: 7
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- - **Haystack dir**: PaulGrahamEssays
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+ library_name: transformers
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+ tags: []
 
 
 
 
 
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  ---
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+ # Model Card for Model ID
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ ## Model Details
 
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+ ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ ## Uses
 
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+ ## Bias, Risks, and Limitations
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+ ### Recommendations
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ ## Training Details
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+ ### Training Data
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+ ### Training Procedure
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+ #### Training Hyperparameters
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+ ## Evaluation
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+ #### Metrics
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+ ### Results
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+ #### Summary
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+ ## Model Examination [optional]
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+ ## Environmental Impact
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+ ## Technical Specifications [optional]
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+ ### Model Architecture and Objective
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+ ## Glossary [optional]
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+ ## More Information [optional]
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+ "vocab_size": 128256
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