Update README.md

README.md

@@ -1,109 +1,59 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- medical
+- science
+- biology
+- chemistry
+- not-for-all-audiences
+license: apache-2.0
+datasets:
+- Locutusque/hercules-v4.0
+language:
+- en
 ---
+# Model Card: Hercules-4.0-Mistral-v0.2-7B
 
-# Model Card for Model ID
+![image/png](https://tse3.mm.bing.net/th/id/OIG1.vnrl3xpEcypR3McLW63q?pid=ImgGn)
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+Hercules-4.0-Mistral-v0.2-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.2. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v3.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v3.0 with enhanced abilities in:
 
+- Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology.
+- Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values.
+- Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more.
 
-## Model Details
+## Intended Uses & Potential Bias
 
-### Model Description
+Hercules-4.0-Mistral-v.02-7B is well-suited to the following applications:
 
-<!-- Provide a longer summary of what this model is. -->
+- Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields.
+- Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines.
+- Code Generation and Execution: Facilitating code execution through function calls, aiding in software development and prototyping.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Important Note: Although Hercules-v3.0 is carefully constructed, it's important to be aware that the underlying data sources may contain biases or reflect harmful stereotypes. Use this model with caution and consider additional measures to mitigate potential biases in its responses.**
 
-- **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]
+## Limitations and Risks
 
-### Model Sources [optional]
+- Toxicity: The dataset contains toxic or harmful examples.
+- Hallucinations and Factual Errors: Like other language models, Hercules-4.0-Mistral-v0.2-7B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise.
+- Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes.
 
-<!-- Provide the basic links for the model. -->
+## Training Procedure
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- This model was trained on 8 kaggle TPUs, using torch xla SPMD for high MXU efficiency. There was no expense on my end (meaning you can reproduce this too!)
+- A learning rate of 5e-06 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.1. A low learning rate was used to prevent exploding gradients.
+- No mixed precision was used, with the default dtype being bfloat16.
+- A total batch size of 64 was used.
+- Trained on 700,000 examples of Hercules-v4.0
+- No model parameters were frozen and no quantization was used.
+- This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: ```<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{user message}<|im_end|>\n<|im_start|>call\n{function call message}<|im_end|>\n<|im_start|>function\n{function response message}<|im_end|>\n<|im_start|>assistant\n{assistant message}</s>```
 
-## 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.
-
-## 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]
+This model was fine-tuned using my TPU-Alignment repository. https://github.com/Locutusque/TPU-Alignment
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 |              Tasks              |Version|Filter|n-shot| Metric |Value |   |Stderr|
 |---------------------------------|-------|------|-----:|--------|-----:|---|-----:|
 |agieval_nous                     |N/A    |none  |     0|acc_norm|0.3578|±  |0.0093|
@@ -135,100 +85,4 @@ Use the code below to get started with the model.
 | - mmlu_flan_cot_fewshot_social_sciences|N/A    |get-answer|     0|exact_match|0.6528|±  |0.0248|
 | - mmlu_flan_cot_fewshot_stem           |N/A    |get-answer|     0|exact_match|0.4925|±  |0.0266|
 |ai2_arc                                 |N/A    |none      |     0|acc        |0.6936|±  |0.0073|
-|                                        |       |none      |     0|acc_norm   |0.6984|±  |0.0074|
-
-
-
-### 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]
-
-<!-- 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]
+|                                        |       |none      |     0|acc_norm   |0.6984|±  |0.0074|