Update README.md

README.md

@@ -2,7 +2,10 @@
 license: apache-2.0
 base_model: pszemraj/tinyllama-1.1b-3T
 tags:
-- generated_from_trainer
+- bees
+- bzz
+- honey
+- oprah winfrey
 metrics:
 - accuracy
 inference:
@@ -16,35 +19,39 @@ inference:
     top_p: 0.95
     epsilon_cutoff: 0.0008
 widget:
-  - text: In beekeeping, the term "queen excluder" refers to
-    example_title: Queen Excluder
-  - text: One way to encourage a honey bee colony to produce more honey is by
-    example_title: Increasing Honey Production
-  - text: The lifecycle of a worker bee consists of several stages, starting with
-    example_title: Lifecycle of a Worker Bee
-  - text: Varroa destructor is a type of mite that
-    example_title: Varroa Destructor
-  - text: In the world of beekeeping, the acronym PPE stands for
-    example_title: Beekeeping PPE
-  - text: The term "robbing" in beekeeping refers to the act of
-    example_title: Robbing in Beekeeping
-  - text: |-
-      Question: What's the primary function of drone bees in a hive?
-      Answer:
-    example_title: Role of Drone Bees
-  - text: To harvest honey from a hive, beekeepers often use a device known as a
-    example_title: Honey Harvesting Device
-  - text: >-
-      Problem: You have a hive that produces 60 pounds of honey per year. You
-      decide to split the hive into two. Assuming each hive now produces at a
-      70% rate compared to before, how much honey will you get from both hives
-      next year?
-
-      To calculate
-    example_title: Beekeeping Math Problem
-  - text: In beekeeping, "swarming" is the process where
-    example_title: Swarming
+- text: In beekeeping, the term "queen excluder" refers to
+  example_title: Queen Excluder
+- text: One way to encourage a honey bee colony to produce more honey is by
+  example_title: Increasing Honey Production
+- text: The lifecycle of a worker bee consists of several stages, starting with
+  example_title: Lifecycle of a Worker Bee
+- text: Varroa destructor is a type of mite that
+  example_title: Varroa Destructor
+- text: In the world of beekeeping, the acronym PPE stands for
+  example_title: Beekeeping PPE
+- text: The term "robbing" in beekeeping refers to the act of
+  example_title: Robbing in Beekeeping
+- text: |-
+    Question: What's the primary function of drone bees in a hive?
+    Answer:
+  example_title: Role of Drone Bees
+- text: To harvest honey from a hive, beekeepers often use a device known as a
+  example_title: Honey Harvesting Device
+- text: >-
+    Problem: You have a hive that produces 60 pounds of honey per year. You
+    decide to split the hive into two. Assuming each hive now produces at a 70%
+    rate compared to before, how much honey will you get from both hives next
+    year?
+
+    To calculate
+  example_title: Beekeeping Math Problem
+- text: In beekeeping, "swarming" is the process where
+  example_title: Swarming
 pipeline_tag: text-generation
+datasets:
+- BEE-spoke-data/bees-internal
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -52,24 +59,20 @@ should probably proofread and complete it, then remove this comment. -->
 
 # TinyLlama-3T-1.1bee
 
-This model is a fine-tuned version of [pszemraj/tinyllama-1.1b-3T](https://huggingface.co/pszemraj/tinyllama-1.1b-3T) on the BEE-spoke-data/bees-internal dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.1640
-- Accuracy: 0.5406
-
-## Model description
 
-More information needed
+A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements:
 
-## Intended uses & limitations
+- start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
+- vastly improved and expanded SoTA beekeeping dataset
 
-More information needed
 
-## Training and evaluation data
+## Model description
 
-More information needed
+This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset.
 
-## Training procedure
+It achieves the following results on the evaluation set:
+- Loss: 2.1640
+- Accuracy: 0.5406
 
 ### Training hyperparameters
 
@@ -106,4 +109,4 @@ The following hyperparameters were used during training:
 - Transformers 4.36.2
 - Pytorch 2.1.0
 - Datasets 2.16.1
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0