Text Generation
PEFT
Safetensors
llama-2
Eval Results
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@@ -25,7 +25,7 @@ This instruction model was built via parameter-efficient QLoRA finetuning of [ll
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  We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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- ### Helpful Links
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  * Model license: Llama 2 Community License Agreement
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  * Basic usage: [notebook](assets/basic_inference_llama_2_70b_dolphin.ipynb)
@@ -33,6 +33,12 @@ We use state-of-the-art [Language Model Evaluation Harness](https://github.com/E
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  * Loss curves: [plot](https://huggingface.co/dfurman/llama-2-70b-dolphin-peft#finetuning-description)
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  * Runtime stats: [table](https://huggingface.co/dfurman/llama-2-70b-dolphin-peft#runtime-tests)
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  ### Example prompts and responses
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  Example 1:
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  <br>
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- ## Model Description
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  The architecture is a modification of a standard decoder-only transformer.
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  | sequence length | 4096 |
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  | grouped-query attention | ✔️ |
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-
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- ## Finetuning Description
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-
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- This model was trained on a single H100 (80 GB PCIe) for about 17 hours using the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.
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- ![loss curves](https://raw.githubusercontent.com/daniel-furman/sft-demos/main/assets/jul_24_23_1_14_00_log_loss_curves_llama-2-70b-dolphin.png)
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- The above loss curve was generated from the run's private wandb.ai log.
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-
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- ## PreTraining Data
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  For more details on the pretraining process, see [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf).
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  The data was tokenized using the [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) tokenizer.
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- ## Limitations and Biases
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  _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
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  This model was trained on various public datasets.
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  While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
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- ## How to Use
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  Basic usage: [notebook](assets/basic_inference_llama_2_70b_dolphin.ipynb)
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  We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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+ ### Helpful links
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  * Model license: Llama 2 Community License Agreement
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  * Basic usage: [notebook](assets/basic_inference_llama_2_70b_dolphin.ipynb)
 
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  * Loss curves: [plot](https://huggingface.co/dfurman/llama-2-70b-dolphin-peft#finetuning-description)
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  * Runtime stats: [table](https://huggingface.co/dfurman/llama-2-70b-dolphin-peft#runtime-tests)
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+ ## Loss curve
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+
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+ ![loss curves](https://raw.githubusercontent.com/daniel-furman/sft-demos/main/assets/jul_24_23_1_14_00_log_loss_curves_llama-2-70b-dolphin.png)
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+ The above loss curve was generated from the run's private wandb.ai log.
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+
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  ### Example prompts and responses
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  Example 1:
 
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  <br>
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+ ## Model description
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  The architecture is a modification of a standard decoder-only transformer.
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  | sequence length | 4096 |
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  | grouped-query attention | ✔️ |
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+ ## PreTraining data
 
 
 
 
 
 
 
 
 
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  For more details on the pretraining process, see [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf).
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  The data was tokenized using the [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) tokenizer.
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+ ## Limitations and biases
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  _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
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  This model was trained on various public datasets.
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  While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
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+ ## How to use
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  Basic usage: [notebook](assets/basic_inference_llama_2_70b_dolphin.ipynb)
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