| | --- |
| | library_name: transformers |
| | tags: [] |
| | --- |
| | |
| | # 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. |
| |
|
| | - **Developed by:** [More Information Needed] |
| | - **Funded by [optional]:** [More Information Needed] |
| | - **Shared by [optional]:** [More Information Needed] |
| | - **Model type:** [More Information Needed] |
| | - **Language(s) (NLP):** [More Information Needed] |
| | - **License:** [More Information Needed] |
| | - **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] |
| | - **Paper [optional]:** [More Information Needed] |
| | - **Demo [optional]:** [More Information Needed] |
| |
|
| | ## Uses |
| |
|
| | ``` python |
| | |
| | import torch |
| | from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
| | import re |
| | |
| | |
| | |
| | model_id = "jaeyoungk/albatross" |
| | bnb_config = BitsAndBytesConfig( |
| | load_in_4bit=True, |
| | bnb_4bit_use_double_quant=True, |
| | bnb_4bit_quant_type="nf4", |
| | bnb_4bit_compute_dtype=torch.bfloat16 |
| | ) |
| | |
| | tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct') |
| | model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map='auto') |
| | |
| | |
| | |
| | def gen(x): |
| | system_prompt = f""" |
| | Make a trading decision based on the following data. |
| | Please respond with a JSON object in the following format: |
| | {{"investment_decision": string, "summary_reason": string, "short_memory_index": number, "middle_memory_index": number, "long_memory_index": number, "reflection_memory_index": number}} |
| | investment_decision must always be one of {{buy, sell, hold}} |
| | """ |
| | |
| | # Tokenizing the input and generating the output |
| | |
| | inputs = tokenizer( |
| | [ |
| | f"<|start_header_id|>system<|end_header_id|>{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>{x}<|end_header_id|>" |
| | ], return_tensors = "pt").to("cuda") |
| | |
| | |
| | gened = model.generate( |
| | **inputs, |
| | max_new_tokens=256, |
| | early_stopping=True, |
| | |
| | ) |
| | |
| | full_text = tokenizer.decode(gened[0]) |
| | |
| | # Finding the second occurrence of 'user<|end_header_id|' |
| | start_phrase = "user<|end_header_id|>" |
| | first_occurrence = full_text.find(start_phrase) |
| | second_occurrence = full_text.find(start_phrase, first_occurrence + len(start_phrase)) |
| | |
| | if second_occurrence == -1: |
| | # If the second occurrence is not found, fallback to using the first occurrence |
| | start_idx = first_occurrence + len(start_phrase) |
| | else: |
| | start_idx = second_occurrence + len(start_phrase) |
| | |
| | # Find the index of the next special token after the start index |
| | end_idx = full_text.find('\\<|eot_id|', start_idx) |
| | |
| | # Extract the text between start_idx and end_idx |
| | extracted_text = full_text[start_idx:end_idx].strip() |
| | |
| | return extracted_text |
| | |
| | # test the model |
| | gen('input your text here') |
| | |
| | ``` |
| |
|
| | ### 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 |
| |
|
| | <!-- 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 |
| |
|
| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | [More Information Needed] |
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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. |
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|
| | ## How to Get Started with the Model |
| |
|
| | Use the code below to get started with the model. |
| |
|
| | [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 |
| |
|
| | <!-- 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 |
| |
|
| | <!-- 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 |
| |
|
| | <!-- 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] |
| | - **Hours used:** [More Information Needed] |
| | - **Cloud Provider:** [More Information Needed] |
| | - **Compute Region:** [More Information Needed] |
| | - **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] |
| |
|
| | <!-- 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] |
| |
|
| | <!-- 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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