Update README.md

README.md

@@ -11,56 +11,39 @@ metrics:
 - rouge
 - bertscore
 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+license: mit
 ---
 
 # Model Card for Model ID
 
-Quantum Research Bot is a model fined tuned over the latest research data in quantum science. It contains data from the second half of 2024 making it more performant than base models.
-
+Quantum Research Bot is a chat model fined tuned over the latest research data in quantum science. It contains data from the second half of 2024 making it more accurate than general-purpose models.
 
 ## 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Nenad Banfic
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model [optional]:**  meta-llama/Meta-Llama-3.1-8B-Instruct
 
 ## 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. -->
+You can use the model to ask questions about the latest developments in quantum science. Below are examples of questions that general-purpose models may answer incorrectly or inadequately, but this model should provide accurate responses.
 
-### 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]
+| Question | Expected answer  | 
+|:---------------------|:--------------|
+| On top of what platform is TensorKrowch built on and where was it created?        | TensorKrowch is built on top of the PyTorch framework and was created at the University of Madrid          |
+| What algorithms does the quantum FIPS 205 deal with?    | The FIPS 205 deals with the stateless hash-based digital signature algorithm (SLH-DSA).          |
+| What is the variance which you can get with polynomial bond dimension in pure quantum states in one dimensional systems?    | The variance that you can get with polynomial bond dimension in pure quantum states in one dimensional systems is as small as ∝ 1 / log N.| 
+| As if September 2024, how many qubits has the quantum Krylov algorithm been demonstrated on experimentally?  | The quantum Krylov algorithm has been demonstrated on up to 56 qubits experimentally.|
+| In the analysis of noise effects in controlled-swap gate circuits, what percentage of errors were eliminated with a dephasing error probability of 10% when using two noisy copies of a quantum state? |   67% of errors were eliminated when using two copies of a quantum state with a dephasing error probability of 10%. ,
 
+  
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Although this model should be able to generalize well, the quantum science terminology and context is very complex, so it might struggle with corrent simplification, hence, should not be used in that context.
 
 [More Information Needed]
 
@@ -78,7 +61,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Please refer to the instructions for the Meta Instruct models; the principle is the same.
 
 [More Information Needed]