ToastyPigeon
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End of training
Browse files- README.md +175 -190
- adapter_model.safetensors +1 -1
README.md
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---
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base_model: unsloth/Mistral-Nemo-Base-2407
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library_name: peft
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---
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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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- **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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<!-- 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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## Uses
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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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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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[More Information Needed]
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## Training Details
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### Training Data
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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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[More Information Needed]
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### Training Procedure
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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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#### 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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#### Speeds, Sizes, Times [optional]
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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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<!-- 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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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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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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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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- **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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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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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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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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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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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.12.0
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---
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base_model: unsloth/Mistral-Nemo-Base-2407
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library_name: peft
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license: apache-2.0
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: adventure-nemo-ws
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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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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# python -m axolotl.cli.preprocess adventure-nemo.yml
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# accelerate launch -m axolotl.cli.train adventure-nemo.yml
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# python -m axolotl.cli.merge_lora adventure-nemo.yml
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base_model: unsloth/Mistral-Nemo-Base-2407
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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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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sequence_len: 8192 # 99% vram
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bf16: auto
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fp16:
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tf32: false
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flash_attention: true
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special_tokens:
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# Data
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dataset_prepared_path: last_run_prepared
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datasets:
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- path: ColumbidAI/adventure-8k
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type: completion
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warmup_steps: 10
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shuffle_merged_datasets: true
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save_safetensors: true
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saves_per_epoch: 4
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save_total_limit: 2
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# WandB
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wandb_project: Nemo-A
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wandb_entity:
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# Iterations
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num_epochs: 1
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# Output
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output_dir: ./adventure-command-r-workspace
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hub_model_id: ToastyPigeon/adventure-nemo-ws
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hub_strategy: "all_checkpoints"
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# Sampling
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sample_packing: true
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pad_to_sequence_len: true
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# Batching
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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gradient_checkpointing: 'unsloth'
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gradient_checkpointing_kwargs:
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use_reentrant: true
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#unsloth_cross_entropy_loss: true
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#unsloth_lora_mlp: true
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#unsloth_lora_qkv: true
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#unsloth_lora_o: true
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# Evaluation
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val_set_size: 0.005
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evals_per_epoch: 5
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eval_table_size:
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eval_max_new_tokens: 256
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eval_sample_packing: false
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eval_batch_size: 1
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# LoRA
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adapter: qlora
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lora_model_dir:
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lora_r: 64
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lora_alpha: 32
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lora_dropout: 0.125
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lora_target_linear:
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lora_fan_in_fan_out:
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lora_target_modules:
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- gate_proj
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- down_proj
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- up_proj
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- q_proj
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- v_proj
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- k_proj
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- o_proj
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lora_modules_to_save:
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# Optimizer
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optimizer: paged_adamw_8bit # adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.00025
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lr_scheduler: cosine_with_min_lr
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lr_scheduler_kwargs:
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min_lr: 0.000025
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weight_decay: 0.01
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max_grad_norm: 20.0
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# Misc
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train_on_inputs: false
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group_by_length: false
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early_stopping_patience:
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local_rank:
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logging_steps: 1
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xformers_attention:
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debug:
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#deepspeed: /workspace/axolotl/deepspeed_configs/zero3.json # previously blank
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fsdp:
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fsdp_config:
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_swiglu: true
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liger_fused_linear_cross_entropy: true
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```
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</details><br>
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# adventure-nemo-ws
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This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1587
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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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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.00025
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- train_batch_size: 4
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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_with_min_lr
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.9422 | 0.0011 | 1 | 2.3948 |
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| 1.8427 | 0.2011 | 189 | 2.2440 |
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| 1.6786 | 0.4021 | 378 | 2.2143 |
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| 1.9847 | 0.6032 | 567 | 2.1799 |
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| 1.8358 | 0.8043 | 756 | 2.1587 |
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180 |
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|
181 |
### Framework versions
|
182 |
|
183 |
+
- PEFT 0.12.0
|
184 |
+
- Transformers 4.45.0.dev0
|
185 |
+
- Pytorch 2.3.1+cu121
|
186 |
+
- Datasets 2.21.0
|
187 |
+
- Tokenizers 0.19.1
|
adapter_model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 912336848
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d657ad555bab9ced432b9f644914b0b7ed197bddd78277e30536e9a97d26ec5c
|
3 |
size 912336848
|