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onuralp
/
Llama-2-13b-QLoRA-02

Text Generation
Transformers
llama
Generated from Trainer
text-generation-inference
Inference Endpoints
4-bit precision
bitsandbytes
Model card Files Community
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  1. README.md +93 -0
  2. adapter_config.json +28 -0
  3. adapter_model.bin +3 -0
  4. checkpoint-699/README.md +219 -0
  5. checkpoint-699/adapter_config.json +28 -0
  6. checkpoint-699/adapter_model.bin +3 -0
  7. checkpoint-699/optimizer.pt +3 -0
  8. checkpoint-699/rng_state.pth +3 -0
  9. checkpoint-699/scheduler.pt +3 -0
  10. checkpoint-699/trainer_state.json +4485 -0
  11. checkpoint-699/training_args.bin +3 -0
  12. config.json +39 -0
  13. special_tokens_map.json +24 -0
  14. tokenizer.model +3 -0
  15. tokenizer_config.json +44 -0
README.md ADDED
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+ ---
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+ base_model: meta-llama/Llama-2-13b-hf
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: qlora-out
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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+ # qlora-out
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+
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+ This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5407
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0004
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 8
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 300
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.8973 | 0.03 | 20 | 0.7029 |
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+ | 0.6828 | 0.06 | 40 | 0.6521 |
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+ | 0.6521 | 0.09 | 60 | 0.6199 |
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+ | 0.7857 | 0.11 | 80 | 0.6066 |
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+ | 0.6208 | 0.14 | 100 | 0.6063 |
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+ | 0.6805 | 0.17 | 120 | 0.5969 |
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+ | 0.5928 | 0.2 | 140 | 0.5989 |
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+ | 0.715 | 0.23 | 160 | 0.5844 |
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+ | 0.5647 | 0.26 | 180 | 0.5979 |
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+ | 0.6778 | 0.29 | 200 | 0.5889 |
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+ | 0.5907 | 0.31 | 220 | 0.5772 |
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+ | 0.5536 | 0.34 | 240 | 0.5917 |
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+ | 0.7422 | 0.37 | 260 | 0.6781 |
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+ | 0.6328 | 0.4 | 280 | 0.5785 |
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+ | 0.5705 | 0.43 | 300 | 0.5720 |
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+ | 0.6124 | 0.46 | 320 | 0.5753 |
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+ | 0.4735 | 0.49 | 340 | 0.6203 |
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+ | 0.4602 | 0.52 | 360 | 0.5772 |
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+ | 0.8571 | 0.54 | 380 | 0.5750 |
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+ | 0.5504 | 0.57 | 400 | 0.6040 |
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+ | 0.6307 | 0.6 | 420 | 0.5796 |
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+ | 0.4782 | 0.63 | 440 | 0.5639 |
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+ | 0.4159 | 0.66 | 460 | 0.5689 |
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+ | 0.6393 | 0.69 | 480 | 0.5661 |
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+ | 0.8243 | 0.72 | 500 | 0.5698 |
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+ | 0.4744 | 0.74 | 520 | 0.5536 |
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+ | 0.4395 | 0.77 | 540 | 0.5536 |
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+ | 0.543 | 0.8 | 560 | 0.5493 |
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+ | 0.4451 | 0.83 | 580 | 0.5421 |
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+ | 0.5384 | 0.86 | 600 | 0.5467 |
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+ | 0.4438 | 0.89 | 620 | 0.5379 |
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+ | 0.4168 | 0.92 | 640 | 0.5398 |
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+ | 0.469 | 0.94 | 660 | 0.5402 |
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+ | 0.6766 | 0.97 | 680 | 0.5407 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "meta-llama/Llama-2-13b-hf",
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+ "bias": "none",
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+ "fan_in_fan_out": null,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "lora_alpha": 64,
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+ "lora_dropout": 0.05,
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+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "r": 64,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "up_proj",
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+ "gate_proj",
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+ "o_proj",
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+ "k_proj",
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+ "q_proj",
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+ "down_proj",
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+ "v_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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checkpoint-699/README.md ADDED
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+ ---
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+ library_name: peft
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+ base_model: meta-llama/Llama-2-13b-hf
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [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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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Data Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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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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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+
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+
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+ ## Training procedure
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+
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - quant_method: bitsandbytes
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: nf4
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+ - bnb_4bit_use_double_quant: True
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+ - bnb_4bit_compute_dtype: bfloat16
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+
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+ ### Framework versions
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+
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+
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+ - PEFT 0.6.0.dev0
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+ "q_proj",
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+ "down_proj",
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+ "v_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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