Update README.md

README.md

@@ -5,7 +5,8 @@ base_model: mistralai/Mistral-7B-v0.1
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Our finetuned Mistral LLM is a large language model specialized for natural language processing tasks, delivering enhanced performance for a 
+wide array of applications, including text classification, question-answering, chatbot services, and more.
 
 
 
@@ -17,13 +18,12 @@ base_model: mistralai/Mistral-7B-v0.1
 
 
 
-- **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]
+- **Developed by:** Basel Anaya, Osama Awad, Yazeed Mshayekh
+- **Funded by [optional]:** Basel Anaya, Osama Awad, Yazeed Mshayekh
+- **Model type:** Autoregressive Language Model
+- **Language(s) (NLP):** English
+- **License:** MIT License
+- **Finetuned from model:** MistralAI's Mistral-7B
 
 ### Model Sources [optional]
 
@@ -35,43 +35,59 @@ base_model: mistralai/Mistral-7B-v0.1
 
 ## 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. -->
-
+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]
+Users can leverage the finetuned Mistral LLM for various NLP tasks right out-of-the-box. Simply interact with the API or load the model locally to experience superior language understanding and generation capabilities. Ideal for developers seeking rapid prototyping and deployment of conversational AI applications.
 
 ### 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]
+Integrate the finetuned Mistral LLM effortlessly into custom applications and pipelines. Utilize the model as a starting point for further refinement, targeting industry-specific lingo, niches, or particular use cases. Seamless compatibility ensures smooth collaboration with adjacent technologies and services.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Limitations exist concerning controversial topics, sensitive data, and scenarios demanding real-time responses. Users should exercise caution when deploying the model in safety-critical situations or regions with strict compliance regulations. Avoid sharing confidential or personally identifiable information with the model.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Address both technical and sociotechnical limitations.
 
 ### 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.
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Further recommendations include cautious assessment of ethical implications, ongoing maintenance, periodic evaluations, and responsible reporting practices.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+import torch
+from transformers import pipeline, AutoTokenizer
+
+# Load the finetuned Mistral LLM
+model_name = "Reverb/Mistral-7B-LoreWeaver"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+generator = pipeline("text-generation", model=model_name, tokenizer=tokenizer)
+
+# Example usage
+input_text = "Once upon a time,"
+num_generated_tokens = 50
+
+response = generator(input_text, max_length=num_generated_tokens, num_return_sequences=1)
+print(f"Generated text:\n{response[0]['generated_text']}")
+
+# Alternatively, for fine-grained control over the generation process
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = generator.generate(
+    inputs["input_ids"].to("cuda"),
+    max_length=num_generated_tokens,
+    num_beams=5,
+    early_stopping=True,
+    temperature=1.2,
+)
+generated_sentence = tokenizer.decode(outputs[0])
+print(f"\nGenerated text with beam search and custom params:\n{generated_sentence}")
+```
 
 ## Training Details