Instructions to use robertou2/task-9-microsoft-Phi-3.5-mini-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use robertou2/task-9-microsoft-Phi-3.5-mini-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct") model = PeftModel.from_pretrained(base_model, "robertou2/task-9-microsoft-Phi-3.5-mini-instruct") - Notebooks
- Google Colab
- Kaggle
Delete checkpoint-36
Browse files- checkpoint-36/README.md +0 -203
- checkpoint-36/adapter_config.json +0 -37
- checkpoint-36/adapter_model.safetensors +0 -3
- checkpoint-36/added_tokens.json +0 -13
- checkpoint-36/optimizer.pt +0 -3
- checkpoint-36/rng_state.pth +0 -3
- checkpoint-36/scheduler.pt +0 -3
- checkpoint-36/special_tokens_map.json +0 -24
- checkpoint-36/tokenizer.json +0 -0
- checkpoint-36/tokenizer.model +0 -3
- checkpoint-36/tokenizer_config.json +0 -133
- checkpoint-36/trainer_state.json +0 -55
- checkpoint-36/training_args.bin +0 -3
checkpoint-36/README.md
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---
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base_model: microsoft/Phi-3.5-mini-instruct
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library_name: peft
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
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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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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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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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## Evaluation
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#### Testing Data
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#### Factors
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
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- PEFT 0.13.2
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- PEFT 0.15.0
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{
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"bias": "none",
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checkpoint-36/tokenizer.model
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checkpoint-36/training_args.bin
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