gouthamsk/mistral_embedded_c

README.md

@@ -15,26 +15,24 @@ model-index:
 
 <!-- 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. -->
-![This is an alt text.](http://biboxlabs.com/wp-content/uploads/2021/07/bibox-labs-logo.png 'Logo.')
 
-For more Info vist [Biboxlabs](https://biboxlabs.com/) or [Red Nerd](https://www.therednerds.com/)
 # mistral_embedded_c
 
 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
-It has been fine-tuned using LoRa (Long Range) technology, enhancing its capabilities for applications requiring long-range communication.
 
 ## Model description
-The `mistral_embedded_c` model is specifically designed for generating accurate embedded C code targeting microcontrollers such as esp32, pic18f, pic10f, and 8051 MCU. It is a fine-tuned variant of the Mistral-7B-Instruct-v0.2 model, customized to cater to the needs of embedded systems development.
+
+More information needed
 
 ## Intended uses & limitations
-The model can be utilized for prompting to generate precise embedded C code suitable for a variety of microcontrollers including esp32, pic18f, pic10f, and 8051 MCU. However, it should be noted that while the model is trained on a diverse dataset encompassing assembly and C code for these microcontrollers, its performance may vary depending on the complexity and specificity of the task.
+
+More information needed
+
 ## Training and evaluation data
 
-The model is trained of the mixed dataset which contains assembly and c code for esp32, pic18f, pic10f, and 8051 MCU.
-Training Data is specifically curated for specific purposes to evaluate the embedded c code. 
+More information needed
 
 ## Training procedure
-The training was done on an A100-40GB accelerator.
 
 ### Training hyperparameters
 
@@ -46,7 +44,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_steps: 0.03
-- training_steps: 500
+- training_steps: 300
 
 ### Training results
 

adapter_config.json

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   "use_dora": false,

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