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Browse files- README.md +135 -0
- adapter_config.json +33 -0
- adapter_model.bin +3 -0
- checkpoint-431/README.md +202 -0
- checkpoint-431/adapter_config.json +33 -0
- checkpoint-431/adapter_model.safetensors +3 -0
- checkpoint-431/optimizer.pt +3 -0
- checkpoint-431/rng_state.pth +3 -0
- checkpoint-431/scheduler.pt +3 -0
- checkpoint-431/trainer_state.json +3070 -0
- checkpoint-431/training_args.bin +3 -0
- config.json +43 -0
- runs/Mar10_21-02-29_e79f8af2b41e/events.out.tfevents.1710104549.e79f8af2b41e.49.0 +3 -0
- special_tokens_map.json +24 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: openlm-research/open_llama_3b_v2
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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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<!-- 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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[<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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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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```yaml
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base_model: openlm-research/open_llama_3b_v2
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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push_dataset_to_hub:
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datasets:
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- path: mhenrichsen/alpaca_2k_test
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type: alpaca
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dataset_prepared_path:
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val_set_size: 0.05
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adapter: qlora
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lora_model_dir:
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sequence_len: 1024
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sample_packing: true
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+
lora_r: 8
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+
lora_alpha: 32
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lora_dropout: 0.05
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lora_target_modules:
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lora_target_linear: true
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lora_fan_in_fan_out:
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+
wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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output_dir: ./qlora-out
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gradient_accumulation_steps: 1
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micro_batch_size: 1
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num_epochs: 1
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optimizer: paged_adamw_32bit
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+
torchdistx_path:
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: false
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fp16: true
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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gptq_groupsize:
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gptq_model_v1:
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warmup_steps: 20
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evals_per_epoch: 4
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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special_tokens:
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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```
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</details><br>
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# qlora-out
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This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1118
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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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: 0.0002
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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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: 20
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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|
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.2567 | 0.0 | 1 | 1.3470 |
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| 1.1738 | 0.25 | 108 | 1.1365 |
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| 1.113 | 0.5 | 216 | 1.1231 |
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| 1.413 | 0.75 | 324 | 1.1118 |
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### Framework versions
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- PEFT 0.9.0
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- Transformers 4.38.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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adapter_config.json
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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": "openlm-research/open_llama_3b_v2",
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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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"loftq_config": {},
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"o_proj",
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"v_proj",
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"down_proj",
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"gate_proj",
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"k_proj",
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"q_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8586987501c13202ba5f626b0ee14248d769c4af5e3097461ce53d12e4b9a39
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size 50982842
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checkpoint-431/README.md
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---
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library_name: peft
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base_model: openlm-research/open_llama_3b_v2
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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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## Model Details
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### Model Description
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15 |
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<!-- Provide a longer summary of what this model is. -->
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|
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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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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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29 |
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<!-- Provide the basic links for the model. -->
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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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### Direct Use
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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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[More Information Needed]
|
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### Downstream Use [optional]
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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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[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
|
65 |
+
|
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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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77 |
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|
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### Training Data
|
79 |
+
|
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<!-- This should link to a Dataset 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
|
85 |
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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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[More Information Needed]
|
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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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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
|
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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+
|
133 |
+
|
134 |
+
|
135 |
+
## Model Examination [optional]
|
136 |
+
|
137 |
+
<!-- Relevant interpretability work for the model goes here -->
|
138 |
+
|
139 |
+
[More Information Needed]
|
140 |
+
|
141 |
+
## Environmental Impact
|
142 |
+
|
143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
+
|
145 |
+
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).
|
146 |
+
|
147 |
+
- **Hardware Type:** [More Information Needed]
|
148 |
+
- **Hours used:** [More Information Needed]
|
149 |
+
- **Cloud Provider:** [More Information Needed]
|
150 |
+
- **Compute Region:** [More Information Needed]
|
151 |
+
- **Carbon Emitted:** [More Information Needed]
|
152 |
+
|
153 |
+
## Technical Specifications [optional]
|
154 |
+
|
155 |
+
### Model Architecture and Objective
|
156 |
+
|
157 |
+
[More Information Needed]
|
158 |
+
|
159 |
+
### Compute Infrastructure
|
160 |
+
|
161 |
+
[More Information Needed]
|
162 |
+
|
163 |
+
#### Hardware
|
164 |
+
|
165 |
+
[More Information Needed]
|
166 |
+
|
167 |
+
#### Software
|
168 |
+
|
169 |
+
[More Information Needed]
|
170 |
+
|
171 |
+
## Citation [optional]
|
172 |
+
|
173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
+
|
175 |
+
**BibTeX:**
|
176 |
+
|
177 |
+
[More Information Needed]
|
178 |
+
|
179 |
+
**APA:**
|
180 |
+
|
181 |
+
[More Information Needed]
|
182 |
+
|
183 |
+
## Glossary [optional]
|
184 |
+
|
185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
+
|
187 |
+
[More Information Needed]
|
188 |
+
|
189 |
+
## More Information [optional]
|
190 |
+
|
191 |
+
[More Information Needed]
|
192 |
+
|
193 |
+
## Model Card Authors [optional]
|
194 |
+
|
195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
## Model Card Contact
|
198 |
+
|
199 |
+
[More Information Needed]
|
200 |
+
### Framework versions
|
201 |
+
|
202 |
+
- PEFT 0.9.0
|
checkpoint-431/adapter_config.json
ADDED
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
1 |
+
{
|
2 |
+
"alpha_pattern": {},
|
3 |
+
"auto_mapping": null,
|
4 |
+
"base_model_name_or_path": "openlm-research/open_llama_3b_v2",
|
5 |
+
"bias": "none",
|
6 |
+
"fan_in_fan_out": null,
|
7 |
+
"inference_mode": true,
|
8 |
+
"init_lora_weights": true,
|
9 |
+
"layers_pattern": null,
|
10 |
+
"layers_to_transform": null,
|
11 |
+
"loftq_config": {},
|
12 |
+
"lora_alpha": 32,
|
13 |
+
"lora_dropout": 0.05,
|
14 |
+
"megatron_config": null,
|
15 |
+
"megatron_core": "megatron.core",
|
16 |
+
"modules_to_save": null,
|
17 |
+
"peft_type": "LORA",
|
18 |
+
"r": 8,
|
19 |
+
"rank_pattern": {},
|
20 |
+
"revision": null,
|
21 |
+
"target_modules": [
|
22 |
+
"o_proj",
|
23 |
+
"v_proj",
|
24 |
+
"down_proj",
|
25 |
+
"gate_proj",
|
26 |
+
"k_proj",
|
27 |
+
"q_proj",
|
28 |
+
"up_proj"
|
29 |
+
],
|
30 |
+
"task_type": "CAUSAL_LM",
|
31 |
+
"use_dora": false,
|
32 |
+
"use_rslora": false
|
33 |
+
}
|
checkpoint-431/adapter_model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7f49815c4ebfdfae3124171bd3814b69b4ae03cbc4bd27f8b759eecce4847f4d
|
3 |
+
size 50899792
|
checkpoint-431/optimizer.pt
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:e6a81e9d166874b12955581f305e0f9531063c32101268fb6ebabf6d690d6b4e
|
3 |
+
size 101919290
|
checkpoint-431/rng_state.pth
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:e766413e4f3b39c1d0ac620807b3bd3fd4dac79e0a0eed6a4a60c5746642e0a6
|
3 |
+
size 14244
|
checkpoint-431/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:23f73a16ac262980457d80b6b9e4834ebbf9e3ee06ac2280222318fcdf9e15a8
|
3 |
+
size 1064
|
checkpoint-431/trainer_state.json
ADDED
@@ -0,0 +1,3070 @@
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