|
--- |
|
license: mit |
|
datasets: |
|
- Locutusque/InstructMix |
|
language: |
|
- en |
|
metrics: |
|
- bleu |
|
- perplexity |
|
pipeline_tag: text-generation |
|
widget: |
|
- text: >- |
|
<|USER|> Design a Neo4j database and Cypher function snippet to Display |
|
Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners. |
|
Implement if/else or switch/case statements to handle different conditions |
|
related to the Consent. Provide detailed comments explaining your control |
|
flow and the reasoning behind each decision. <|ASSISTANT|> |
|
- text: >- |
|
<|USER|> Write me a story about a magical place. <|ASSISTANT|> |
|
- text: >- |
|
<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> |
|
- text: >- |
|
<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> |
|
- text: >- |
|
<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> |
|
inference: |
|
parameters: |
|
temperature: 0.5 |
|
do_sample: True |
|
top_p: 0.5 |
|
top_k: 30 |
|
max_new_tokens: 250 |
|
repetition_penalty: 1.15 |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
This a fine-tuned version of gpt2 on Locutusque/InstructMix. |
|
|
|
## Model Details |
|
This model performs significantly better than Locutusque/gpt2-large-conversational. Here are the training results: |
|
|
|
|
|
- BLEU - 30 |
|
- Perplexity - 5 |
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
|
|
|
|
- **Developed by:** Locutusque |
|
- **Shared by [optional]:** [More Information Needed] |
|
- **Model type:** GPT-2 |
|
- **Language(s) (NLP):** English |
|
- **License:** mit |
|
- **Finetuned from model [optional]:** GPT-2 |
|
|
|
### Model Sources [optional] |
|
|
|
<!-- Provide the basic links for the model. --> |
|
|
|
- **Repository:** [More Information Needed] |
|
- **Paper [optional]:** [More Information Needed] |
|
- **Demo [optional]:** [More Information Needed] |
|
|
|
## Uses |
|
|
|
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
This model is designed to follow instructions, or partake in conversations. |
|
|
|
### Direct Use |
|
|
|
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
|
|
Instruction-following or conversational. |
|
|
|
### Downstream Use [optional] |
|
|
|
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
|
|
|
[More Information Needed] |
|
|
|
### Out-of-Scope Use |
|
|
|
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
|
|
This model struggles to write complex code, and I only recommend simple code from this model. |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
|
|
This model will most likely produce false information, especially about history. Make sure to confirm the responses this model makes. |
|
|
|
### 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. |
|
|
|
```python |
|
import torch |
|
from transformers import GPT2Tokenizer, GPT2LMHeadModel |
|
|
|
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large-conversational-retrain') |
|
model = GPT2LMHeadModel.from_pretrained('gpt2-large-conversational-retrain') |
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
model.to(device) |
|
def generate_text(model, tokenizer, prompt, max_length=1024): |
|
prompt = f'<|USER|> {prompt} <|ASSISTANT|> ' |
|
input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device) |
|
attention_mask = torch.ones_like(input_ids).to(device) |
|
output = model.generate(input_ids, |
|
max_length=max_length, |
|
do_sample=True, |
|
temperature=0.3, |
|
top_k=23, |
|
top_p=0.7, |
|
repetition_penalty=1.176, |
|
pad_token_id=tokenizer.pad_token_id, |
|
eos_token_id=tokenizer.eos_token_id, |
|
attention_mask=attention_mask) |
|
output_ids = tokenizer.decode(output[0], skip_special_tokens=False) |
|
return output_ids |
|
# Loop to interact with the model |
|
while True: |
|
prompt = input("Enter a prompt (or 'q' to quit): ") |
|
if prompt == "q": |
|
break |
|
output_text = generate_text(model, tokenizer, prompt) |
|
print(output_text) |
|
``` |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
|
|
https://huggingface.co./datasets/Locutusque/InstructMix |
|
|
|
This model has so far been trained on 600,000 examples of the linked data, with more training sessions to come. |
|
|
|
### Training Procedure |
|
|
|
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
|
|
|
#### Preprocessing [optional] |
|
|
|
[More Information Needed] |
|
|
|
|
|
#### Training Hyperparameters |
|
|
|
- **Training regime:** fp16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
|
|
|
#### Speeds, Sizes, Times [optional] |
|
|
|
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
|
|
[More Information Needed] |
|
|
|
## Evaluation |
|
|
|
<!-- This section describes the evaluation protocols and provides the results. --> |
|
|
|
### Testing Data, Factors & Metrics |
|
|
|
#### Testing Data |
|
|
|
<!-- This should link to a Data Card if possible. --> |
|
|
|
[More Information Needed] |
|
|
|
#### Factors |
|
|
|
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
|
[More Information Needed] |
|
|
|
#### Metrics |
|
|
|
<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
|
- BLEU = 30 |
|
- Perplexity = 5 |
|
|
|
### Results |
|
|
|
[More Information Needed] |
|
|
|
#### Summary |
|
|
|
|
|
|
|
## Model Examination [optional] |
|
|
|
<!-- Relevant interpretability work for the model goes here --> |
|
|
|
[More Information Needed] |
|
|
|
## Environmental Impact |
|
|
|
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
|
|
|
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). |
|
|
|
- **Hardware Type:** [More Information Needed] |
|
- **Hours used:** [More Information Needed] |
|
- **Cloud Provider:** [More Information Needed] |
|
- **Compute Region:** [More Information Needed] |
|
- **Carbon Emitted:** [More Information Needed] |
|
|
|
## Technical Specifications [optional] |
|
|
|
### Model Architecture and Objective |
|
|
|
[More Information Needed] |
|
|
|
### Compute Infrastructure |
|
|
|
[More Information Needed] |
|
|
|
#### Hardware |
|
|
|
[More Information Needed] |
|
|
|
#### Software |
|
|
|
[More Information Needed] |
|
|
|
## Citation [optional] |
|
|
|
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
|
**BibTeX:** |
|
|
|
[More Information Needed] |
|
|
|
**APA:** |
|
|
|
[More Information Needed] |
|
|
|
## Glossary [optional] |
|
|
|
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
|
|
|
[More Information Needed] |
|
|
|
## More Information [optional] |
|
|
|
[More Information Needed] |
|
|
|
## Model Card Authors [optional] |
|
|
|
[More Information Needed] |
|
|
|
## Model Card Contact |
|
|
|
[More Information Needed] |
