Instructions to use athirdpath/Zeta-17b_LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use athirdpath/Zeta-17b_LORA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("athirdpath/Epsilon-17b") model = PeftModel.from_pretrained(base_model, "athirdpath/Zeta-17b_LORA") - Notebooks
- Google Colab
- Kaggle
Commit ·
8f34a4c
1
Parent(s): bf3a588
Delete checkpoint-48
Browse files- checkpoint-48/README.md +0 -204
- checkpoint-48/adapter_config.json +0 -31
- checkpoint-48/adapter_model.safetensors +0 -3
- checkpoint-48/optimizer.pt +0 -3
- checkpoint-48/rng_state.pth +0 -3
- checkpoint-48/scheduler.pt +0 -3
- checkpoint-48/trainer_state.json +0 -325
- checkpoint-48/training_args.bin +0 -3
checkpoint-48/README.md
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---
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library_name: peft
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base_model: athirdpath/Epsilon-17b
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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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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [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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## 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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[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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## 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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<!-- 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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- **Carbon Emitted:** [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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#### Hardware
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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 0.7.2.dev0
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"alpha_pattern": {},
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"base_model_name_or_path": "athirdpath/Epsilon-17b",
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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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"peft_type": "LORA",
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"r": 32,
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"rank_pattern": {},
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"target_modules": [
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version https://git-lfs.github.com/spec/v1
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size 627
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checkpoint-48/training_args.bin
DELETED
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@@ -1,3 +0,0 @@
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| 1 |
-
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:928ac6942736715b31945b1e93bbacd79dad0c70fdbeca8e29e7b2258729a2fb
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| 3 |
-
size 4667
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