Update README.md

README.md

@@ -1,4 +1,147 @@
 ---
 license: other
-commercial: false
+datasets:
+- tatsu-lab/alpaca
+language:
+- en
+library_name: transformers
 ---
+
+
+# Model Card for `chopt-research-125m`
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+AI Squared's `chopt-research-125m` is a large language 
+model which is derived from Meta AI's Open Pre-trained Transformer language modelsand fine-tuned on a single GPU on a corpus of 50k records ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) to help it exhibit chat-based capabilities.
+
+The ChOPT family of models from AI Squared are licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+While `chopt-research-125m` 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:** Other
+- **Finetuned from model:** OPT
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+**`chopt-research-125m` is not a state-of-the-art language model.** `chopt-research-125m` is an experimental technology and is not designed for use in any
+environment other than for research purposes. 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 `chopt-research-125m` 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/chopt-research-125m'
+
+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
+```
+
+### Model Performance Metrics
+
+We present the results from various model benchmarks on the EleutherAI LLM Evaluation Harness for all models in the DLite family.
+Model results are sorted by mean score, ascending, to provide an ordering. These metrics serve to further show that none of the DLite models are
+state of the art, but rather further show that chat-like behaviors in LLMs can be trained almost independent of model size.
+
+| Model               |   openbookqa |   arc_easy |   winogrande |   hellaswag |   arc_challenge |     piqa |    boolq |
+|:--------------------|-------------:|-----------:|-------------:|------------:|----------------:|---------:|---------:|
+| chopt-125m          |        0.178 |   0.443182 |     0.501973 |    0.294165 |        0.197099 | 0.630577 | 0.476758 |
+| chopt-research-125m |        0.17  |   0.436027 |     0.503552 |    0.294762 |        0.205631 | 0.62568  | 0.48685  |
+| opt-125m            |        0.166 |   0.435606 |     0.501973 |    0.291775 |        0.190273 | 0.6284   | 0.554434 |
+| chopt-350m          |        0.178 |   0.450758 |     0.508287 |    0.325334 |        0.21843  | 0.650707 | 0.559633 |
+| opt_350m            |        0.176 |   0.441077 |     0.52644  |    0.320056 |        0.207338 | 0.645267 | 0.57737  |
+| chopt-research-350m |        0.172 |   0.462542 |     0.514601 |    0.327524 |        0.235495 | 0.643634 | 0.589908 |
+| opt-1.3b            |        0.234 |   0.569865 |     0.596685 |    0.414957 |        0.232935 | 0.718172 | 0.577676 |
+| chopt-research-1_3b |        0.232 |   0.564815 |     0.59116  |    0.424716 |        0.276451 | 0.713275 | 0.634557 |
+| chopt-1_3b          |        0.236 |   0.569444 |     0.584057 |    0.42621  |        0.268771 | 0.723069 | 0.658104 |
+| opt-2.7b            |        0.25  |   0.608165 |     0.608524 |    0.458176 |        0.267918 | 0.738303 | 0.603058 |
+| chopt-2_7b          |        0.276 |   0.616582 |     0.601421 |    0.472615 |        0.288396 | 0.75136  | 0.552294 |
+| chopt-research-2_7b |        0.262 |   0.610269 |     0.625099 |    0.458176 |        0.295222 | 0.742111 | 0.636697 |