aisquared
/

dlite-v1-124m

@@ -10,8 +10,12 @@ library_name: transformers
 <!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 ## Model Details
 ### Model Description
@@ -20,20 +24,12 @@ This modelcard aims to be a base template for new models. It has been generated
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 ## Uses
@@ -45,10 +41,6 @@ This modelcard aims to be a base template for new models. It has been generated
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-### Downstream Use [optional]
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 ### Out-of-Scope Use
@@ -61,143 +53,81 @@ This modelcard aims to be a base template for new models. It has been generated
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 <!-- Provide a quick summary of what the model is/does. -->
+AI Squared's `dlite-v1-124m` ([blog post](https://medium.com/ai-squared/introducing-dlite-a-lightweight-chatgpt-like-model-based-on-dolly-deaa49402a1f)) 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 T4 GPU on a corpos of 50k records
+([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) to help it exhibit chat-based capabilities.
+While `dlite-v1-124m` 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 is much more accessible than previously thought.
 ## Model Details
 ### Model Description
+- **Developed by:** AI Squared, Inc.
+- **Shared by [optional]:** AI Squared, Inc.
+- **Model type:** Large Language Model
+- **Language(s) (NLP):** EN
+- **License:** Apache v2.0
+- **Finetuned from model [optional]:** GPT-2
 ## Uses
 [More Information Needed]
 [More Information Needed]
 ### Out-of-Scope Use
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+**`dlite-v1-124m` is not a state-of-the-art language model.** `dlite-v1-124m` 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
+### Load Model and Tokenizer from this Repository Using the `transformers` Package
+```python
+from transfomrers import AutoModelForCausalLM, AutoTokenizer
+model_id = 'aisquared/dlite-v1-124m'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+```
+### Create the Prompt Format and Other Variables
+```python
+PROMPT_FORMAT = """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
+    """
+    ids = tokenizer(PROMPT_FORMAT.format(instruction = instruction), return_tensors = 'pt').input_ids
+    response_id = tokenizer.encode(RESPONSE_KEY)[0]
+    end_id = tokenizer.encode(END_KEY)[0]
+    tokens = model.generate(
+        ids,
+        pad_token_id = tokenizer.pad_token_id,
+        eos_token_id = end_id,
+        do_sample = do_sample,
+        max_new_tokens = max_new_tokens,
+        top_p = top_p,
+        top_k = top_k,
+        **kwargs
+    )[0].cpu()
+    res_pos = np.where(tokens == response_id)[0]
+    if len(res_pos) == 0:
+        return None
+    res_pos = res_pos[0]
+    end_pos = np.where(tokens == end_id)[0]
+    if len(end_pos) > 0:
+        end_pos = end_pos[0]
+    else:
+        end_pos = None
+    return tokenizer.decode(tokens[res_pos + 1 : end_pos]).strip()
+```