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README.md

@@ -1,199 +1,67 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
 ---
+# DE-LM-7B
 
-# Model Card for Model ID
+DE-LM-7B is a 7.04 billion parameter decoder-only text generation model, released under the Apache 2.0 license. 
+This is an instruction tuned model built on top of Deci/DeciLM-7B fine-tuned for data filtering and API generation.
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-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]
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
 
-### Model Sources [optional]
+## Model Architecture
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+| Parameters | Layers | Heads  | Sequence Length  | GQA num_key_value_heads*  |
+|:----------|:----------|:----------|:----------|:----------|
+| 7.04 billion    | 32    | 32    | 8192   | Variable  |
 
 ## 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. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### 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. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
+The model is intended for commercial and research use in English and can be fine-tuned for various tasks and languages.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### 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:** [More Information Needed] <!--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 Dataset 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. -->
-
-[More Information Needed]
-
-### 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]
+```bibtex
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+model_name = "dataequity/DE-LM-7B"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True).to(device)
 
-## More Information [optional]
+inputs = tokenizer.encode("List the top 10 financial APIs", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
+print(tokenizer.decode(outputs[0]))
 
-[More Information Needed]
+# The model can also be used via the text-generation pipeline interface
+from transformers import pipeline
+generator = pipeline("text-generation", "dataequity/DE-LM-7B", torch_dtype="auto", trust_remote_code=True, device=device)
+outputs = generator("List the top 10 financial APIs", max_new_tokens=100, do_sample=True, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
 
-## Model Card Authors [optional]
+## Ethical Considerations and Limitations
 
-[More Information Needed]
+DE-LM-7B is a new technology that comes with inherent risks associated with its use. 
+The testing conducted so far has been primarily in English and does not encompass all possible scenarios. 
+Like those of all large language models, DE-LM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DE-LM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment.
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{DeciFoundationModels,
+title = {DeciLM-7B},
+author = {DeciAI Research Team},
+year = {2023}
+url={https://huggingface.co/Deci/DeciLM-7B},
+}
+```