Update README.md

README.md

@@ -5,24 +5,71 @@ tags:
 - generated_from_trainer
 metrics:
 - accuracy
-model-index:
-- name: TinyLlama-1.1B-intermediate-step-240k-503b-bees-internal-2048ctx-v3
-  results: []
+inference:
+  parameters:
+    max_new_tokens: 64
+    do_sample: true
+    repetition_penalty: 1.1
+    no_repeat_ngram_size: 5
+    eta_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
+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
-should probably proofread and complete it, then remove this comment. -->
 
-# TinyLlama-1.1B-intermediate-step-240k-503b-bees-internal-2048ctx-v3
+# TinyLlama-1.1bee
+
+
+## Details
 
 This model is a fine-tuned version of [PY007/TinyLlama-1.1B-intermediate-step-240k-503b](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b) on the None dataset.
 It achieves the following results on the evaluation set:
 - Loss: 2.4285
 - Accuracy: 0.4969
 
-## Model description
 
-More information needed
+```
+***** eval metrics *****
+  eval_accuracy           =     0.4972                                              
+  eval_loss               =     2.4283
+  eval_runtime            = 0:00:53.12
+  eval_samples            =        239
+  eval_samples_per_second =      4.499
+  eval_steps_per_second   =      1.129
+  perplexity              =    11.3391
+```
 
 ## Intended uses & limitations
 
@@ -47,21 +94,3 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.03
 - num_epochs: 2.0
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.5642        | 0.34  | 50   | 2.5053          | 0.4863   |
-| 2.5018        | 0.68  | 100  | 2.4512          | 0.4934   |
-| 2.246         | 1.02  | 150  | 2.4317          | 0.4961   |
-| 2.2254        | 1.36  | 200  | 2.4333          | 0.4964   |
-| 2.154         | 1.7   | 250  | 2.4285          | 0.4969   |
-
-
-### Framework versions
-
-- Transformers 4.34.0.dev0
-- Pytorch 2.2.0.dev20230914+cu121
-- Datasets 2.14.5
-- Tokenizers 0.13.3