Update README.md

README.md

@@ -5,6 +5,14 @@ tags:
 model-index:
 - name: Bloom-Alpaca-560m
   results: []
+widget:
+- text: |-
+    ### Instruction:
+    Write a poem about pancakes.
+
+    ### Response:
+datasets:
+- tatsu-lab/alpaca
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -12,21 +20,111 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Bloom-Alpaca-560m
 
-This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on an unknown dataset.
+This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset. This is a test to check if the concept of Databrick's [databricks/dolly-v1-6b](https://huggingface.co/databricks/dolly-v1-6b) can be applied to smaller models.
 
 ## Model description
 
-More information needed
+This is an LLM trained to follow instructions, similar to ChatGPT, except much much smaller.
+
+## Sample Use
+
+```python
+from transformers import pipeline
+
+generator = pipeline('text-generation', model='linkanjarad/Bloom-Alpaca-560m')
+
+prompt = '''### Instruction:
+Who is the first person on the moon?
+
+### Response:'''
+
+output = generator(prompt, do_sample=True, min_length=10, max_length=50, temperature=0.5)
+print(output[0]['generated_text'])
 
 ## Intended uses & limitations
 
-More information needed
+The model works best when given input in the format of the dataset it was trained on. Here is an example of the format of the [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset:
+
+```
+### Instruction:
+What are the three primary colors?
+
+### Response:
+The three primary colors are red, blue, and yellow.
+```
+
+Here are a few sample model outputs:
+
+```
+### Instruction:
+Write a poem about pancakes in the style of Dr. Suess.
+
+### Response:
+Gently bake the delicious cup of pancake goodness away
+In the sweet light of a sunken window
+Stay away from those who take it for granted
+The magic of those who will bake and preserve
+The sweetness of baked pancakes.
+Once more I'm gonna bake my dreams for ever
+No matter what my journey might bring
+No matter how hard I come
+```
+
+```
+### Instruction:
+Generate a program in Python to generate prime numbers.
+
+### Response:
+"""
+Generate a program in Python to generate prime numbers.
+"""
+
+import random
+import math
+import math.exp
+
+def generate_primes():
+    m = random.randint(1, 10)
+    result = random.randint(1, 10)
+    
+    for i in range(m):
+        result = generate_primes()
+        if result == result:
+            break
+    return result
+    
+```
+
+```
+### Instruction:
+How was the steam engine invented?
+
+### Response:
+The steam engine was invented by William George Taylor, a French engineer and inventor who used the technology of steam turbines to create engines. Taylor first designed a turbine that used a rotating disc with a spiral gear that accelerated the air through an annular passage. By expanding the annular passage, Taylor's turbine gained power and became a powerful engine. Taylor then used the spiral gear to create a turbine that drove a rail with a
+```
+
+```
+### Instruction:
+Generate a list of marketing strategies to promote a new mobile app.
 
-## Training and evaluation data
+### Response:
+1. Create a campaign to promote the app's launch.
+2. Provide a promotional video and posters.
+3. Create a mobile app store campaign.
+4. Publish a newsletter to attract potential customers.
+5. Make a list of key features of the app.
+6. Establish a referral program.
+7. Attend marketing conferences and events.
+8. Participate in customer surveys.
+9. Offer loyalty rewards and discounts.
+10. Utilize social media to promote the app.
+11. Create a user-friendly advertising campaign.
+12. Utilize social media to promote the app.
+```
 
-More information needed
+## Training Procedure
 
-## Training procedure
+The model was trained with Huggingface's 🤗 Trainer for about 1 epoch.
 
 ### Training hyperparameters
 
@@ -40,7 +138,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.15
-- num_epochs: 3
+- num_epochs: 1
 
 ### Framework versions