Update README.md

README.md

@@ -1,3 +1,132 @@
 ---
 license: apache-2.0
+datasets:
+- aisquared/databricks-dolly-15k
+language:
+- en
+library_name: transformers
 ---
+
+
+# Model Card for `dlite-v2-355m`
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+AI Squared's `dlite-v2-355m` is a large language 
+model which is derived from OpenAI's smallest [GPT-2](https://huggingface.co/gpt2) model and fine-tuned on a single GPU on a corpus of 15k records
+([Databricks' "Dolly 15k" Dataset](https://huggingface.co/datasets/aisquared/databricks-dolly-15k)) to help it exhibit chat-based capabilities.
+
+Just like [Databricks' Dolly V2 models](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm),
+`dlite-v2-355m` (and all other members of the `dlite-v2` family) is licensed for both **research and commercial use.** We are extremely grateful 
+for the work that Databricks has done to create the `databricks-dolly-15k` dataset, for without it we would not be able to create and release this
+model under such an open and permissive license.
+
+While `dlite-v2-355m` is **not a state-of-the-art model**, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply
+is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought. 
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** AI Squared, Inc.
+- **Shared by:** AI Squared, Inc.
+- **Model type:** Large Language Model
+- **Language(s) (NLP):** EN
+- **License:** Apache v2.0
+- **Finetuned from model:** GPT-2
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+**`dlite-v2-355m` is not a state-of-the-art language model.** `dlite-v2-355m` is an experimental technology, and as with any experimental technology, 
+AI Squared urges potential users of this technology to test its capabilities thoroughly before usage.
+Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include,
+but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations.
+Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology.
+
+
+## Usage
+
+The code below shows how to use `dlite-v2-355m` in the way which it was trained.  While the model can be used "out of the box" using the
+`transformers` library, using the function defined below to create a response from the model will achieve better results.
+
+### Load Model and Tokenizer from this Repository Using the `transformers` Package
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import numpy as np
+import re
+
+model_id = 'aisquared/dlite-v2-355m'
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side = 'left')
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code = True, device_map = 'auto')
+```
+
+
+### Create the Prompt Format and Other Variables
+
+```python
+PROMPT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+"""
+
+END_KEY = '### End'
+RESPONSE_KEY = '### Response:\n'
+```
+
+
+### Create a Function to Retrieve a Response
+
+```python
+def create_response(
+        instruction,
+        model,
+        tokenizer,
+        do_sample = True,
+        max_new_tokens = 256,
+        top_p = 0.92,
+        top_k = 0,
+        **kwargs
+):
+    """
+    Create a response from the model by using a formatted prompt
+    """
+    input_ids = tokenizer(
+        PROMPT.format(instruction=instruction), return_tensors="pt"
+    ).input_ids
+
+    gen_tokens = model.generate(
+        input_ids,
+        pad_token_id=tokenizer.pad_token_id,
+        do_sample=do_sample,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        top_k=top_k,
+        **kwargs,
+    )
+    decoded = tokenizer.batch_decode(gen_tokens)[0]
+
+    # The response appears after "### Response:".  The model has been trained to append "### End" at the end.
+    m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", decoded, flags=re.DOTALL)
+
+    response = None
+    if m:
+        response = m.group(1).strip()
+    else:
+        # The model might not generate the "### End" sequence before reaching the max tokens.  In this case, return
+        # everything after "### Response:".
+        m = re.search(r"#+\s*Response:\s*(.+)", decoded, flags=re.DOTALL)
+        if m:
+            response = m.group(1).strip()
+        else:
+            pass
+    return response
+```