Upload re-trained model

README.md

@@ -13,6 +13,8 @@ pipeline_tag: text-generation
 # Home 3B v2
 The "Home" model is a fine tuning of the Phi-2 model from Microsoft.  The model is able to control devices in the user's house as well as perform basic question and answering.  The fine tuning dataset is a combination of the [Cleaned Stanford Alpaca Dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) as well as a [custom curated dataset](https://github.com/acon96/home-llm) designed to teach the model function calling. V2 of the model has a fixed chatml format, JSON function calling syntax, and support for more Home Assitant entity types.
 
+**NOTE**: This is a re-training as the first attempt did not perform very well for some reason.
+
 The model is quantized using Lama.cpp in order to enable running the model in super low resource environments that are common with Home Assistant installations such as Rapsberry Pis.
 
 The model can be used as an "instruct" type model using the ChatML prompt format. The system prompt is used to provide information about the state of the Home Assistant installation including available devices and callable services.
@@ -20,11 +22,12 @@ The model can be used as an "instruct" type model using the ChatML prompt format
 Example "system" prompt: 
 ```
 You are 'Al', a helpful AI Assistant that controls the devices in a house. Complete the following task as instructed with the information provided only.
-Services: light.turn_off, light.turn_on, fan.turn_on, fan.turn_off
+Services: light.turn_off(), light.turn_on(brightness,rgb_color), fan.turn_on(), fan.turn_off()
 Devices:
-light.office 'Office Light' = on
+light.office 'Office Light' = on;80%
 fan.office 'Office fan' = off
-light.kitchen 'Kitchen Light' = on
+light.kitchen 'Kitchen Light' = on;80%;red
+light.bedroom 'Bedroom Light' = off
 ```
 
 Output from the model will consist of a response that should be relayed back to the user, along with an optional code block that will invoke different Home Assistant "services". The output format from the model for function calling is as follows:
@@ -46,6 +49,9 @@ assistant If Mary is 7 years old, then you are 10 years old (7+3=10).
 ## Training
 The model was trained as a LoRA on an RTX 3090 (24GB). The LoRA has rank = 32, alpha = 64, targets the `fc1,fc2,q_proj,v_proj,out_proj` modules and "saves" the `embed_tokens,lm_head` modules The embedding weights were "saved" and trained normally along with the rank matricies in order to train the newly added tokens to the embeddings. The full model is merged together at the end.
 
+## Evaluation
+This model acheives a 87.31% score for JSON function calling accuracy on the test dataset.
+
 ## Datasets
 Snythetic Dataset for SFT - https://github.com/acon96/home-llm  
 Stanford Alpaca Cleaned - https://huggingface.co/datasets/yahma/alpaca-cleaned