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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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``` python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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import re |
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model_id = "jaeyoungk/albatross" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct') |
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map='auto') |
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def gen(x): |
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system_prompt = f""" |
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Make a trading decision based on the following data. |
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Please respond with a JSON object in the following format: |
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{{"investment_decision": string, "summary_reason": string, "short_memory_index": number, "middle_memory_index": number, "long_memory_index": number, "reflection_memory_index": number}} |
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investment_decision must always be one of {{buy, sell, hold}} |
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""" |
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# Tokenizing the input and generating the output |
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inputs = tokenizer( |
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[ |
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f"<|start_header_id|>system<|end_header_id|>{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>{x}<|end_header_id|>" |
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], return_tensors = "pt").to("cuda") |
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gened = model.generate( |
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**inputs, |
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max_new_tokens=256, |
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early_stopping=True, |
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) |
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full_text = tokenizer.decode(gened[0]) |
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# Finding the second occurrence of 'user<|end_header_id|' |
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start_phrase = "user<|end_header_id|>" |
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first_occurrence = full_text.find(start_phrase) |
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second_occurrence = full_text.find(start_phrase, first_occurrence + len(start_phrase)) |
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if second_occurrence == -1: |
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# If the second occurrence is not found, fallback to using the first occurrence |
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start_idx = first_occurrence + len(start_phrase) |
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else: |
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start_idx = second_occurrence + len(start_phrase) |
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# Find the index of the next special token after the start index |
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end_idx = full_text.find('\\<|eot_id|', start_idx) |
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# Extract the text between start_idx and end_idx |
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extracted_text = full_text[start_idx:end_idx].strip() |
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return extracted_text |
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# test the model |
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gen('input your text here') |
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``` |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
