shawhin/shawgpt-ft

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 ---
-library_name: peft
 base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-## Uses
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-### Direct Use
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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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
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-#### Factors
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-### Results
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-#### Summary
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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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-## Technical Specifications [optional]
-### Model Architecture and Objective
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 ### Framework versions
-- PEFT 0.8.2

 ---
+license: apache-2.0
 base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
+tags:
+- generated_from_trainer
+model-index:
+- name: shawgpt-ft
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# shawgpt-ft
+This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7578
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 4.5944        | 0.92  | 3    | 3.9680          |
+| 4.0632        | 1.85  | 6    | 3.4430          |
+| 3.4599        | 2.77  | 9    | 2.9820          |
+| 2.2563        | 4.0   | 13   | 2.5456          |
+| 2.6364        | 4.92  | 16   | 2.2781          |
+| 2.2961        | 5.85  | 19   | 2.0657          |
+| 2.0692        | 6.77  | 22   | 1.9161          |
+| 1.4295        | 8.0   | 26   | 1.8044          |
+| 1.8307        | 8.92  | 29   | 1.7627          |
+| 1.2551        | 9.23  | 30   | 1.7578          |
 ### Framework versions
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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