jtatman
/

Reyna-Mini-1.8B-v0.2-function-call-laser

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- function calling
+- laser
+license: apache-2.0
+datasets:
+- jtatman/glaive_function_calling_v2_filtered_10k
 ---
+# Model Card
+This is a laser fine tuning of Aloobun's [great 1.5b param reyna mini model](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2).
 ### Model Description
+This model is quite conversational - even a bit more so after laser tuning even using Peft. The function calling is mediocre, but will be improved in future versions.
 ## Uses
+As Aloobun's model is well performing and impressive on it's own, I decided to add some function calling while practicing the LaserRMT technique.
 ### Direct Use
+Chat
+Conversational
+Text Generation
+Function Calling
 ## Bias, Risks, and Limitations
+This model will take over your house, borrow your car, talk badly to your family, and generally make everything incrementally worse. If you use it for nefarious purposes.
 ### Recommendations
+Use at your own risk. It's a great small model, owing to the base model before tuning.
 ## Training Details
 ### Training Data
+{
+  "eval/loss": 2.1797242164611816,
+  "_timestamp": 1708624900.2239263,
+  "_runtime": 20945.370138406754,
+  "train/train_loss": 2.515587423102269,
+  "train/global_step": 918,
+  "train/train_steps_per_second": 0.044,
+  "train/loss": 2.2062,
+  "train/learning_rate": 0,
+  "train/train_samples_per_second": 1.403,
+  "train/train_runtime": 20945.6359,
+  "eval/steps_per_second": 4.867,
+  "eval/samples_per_second": 4.867,
+  "_step": 923,
+  "train/epoch": 2.98,
+  "eval/runtime": 41.0972,
+  "train/grad_norm": 0.2638521194458008,
+  "train/total_flos": 141790931224363000
+}
 ### Training Procedure
+[LaserRMT](https://github.com/cognitivecomputations/laserRMT) was used to refine the weights, using the 16 highest scored weights specifically by noise-to-ratio analysis.
+This technique avoids training unnecessarily low-performng weights that can turn to garbage. By pruning these weights, the model size is decreased slightly.
+![axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/image/axolotl-badge-web.png?raw=true)
+Axolotl was used for training and dataset tokenization.
+#### Preprocessing [optional]
+Dataset was formatted in ShareGpt format for the purposes of using with Axolotl, in conversational format.
 #### Training Hyperparameters
+lora_r: 64
+lora_alpha: 16
+lora_dropout: 0.05
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.00025