Update README.md

README.md

@@ -1,199 +1,164 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- fka/awesome-chatgpt-prompts
+base_model:
+- unsloth/mistral-7b-instruct-v0.2-bnb-4bit
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+---
 
+# Model Card for Mistral-7B Instruct v0.2 Finetuned Prompt Generator
 
+This model is fine-tuned for generating contextually relevant prompts for various scenarios and domains, helping users craft detailed and effective prompt instructions.
 
 ## 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]
+This model is a fine-tuned version of [Mistral-7B-Instruct-v0.2-bnb-4bit] aimed at providing high-quality prompt generation across diverse topics.
+It excels in understanding input instructions and generating structured prompt that fit various creative, professional, and instructional needs.
 
-### 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:** Abhinav Sarkar
+- **Shared by:** abhinavsarkar
+- **Model type:** Causal Language Model
+- **Languages:** English
+- **Finetuned from model:** Mistral-7B-Instruct-v0.2-bnb-4bit
 
 ## 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. -->
+This model is designed for generating context-specific prompts to assist with content creation, task descriptions, and crafting prompts for AI-based systems.
+It can be utilized to streamline processes in areas such as software development, customer interaction, and creative writing.
 
-[More Information Needed]
+### Downstream Use
 
-## Bias, Risks, and Limitations
+This model can be incorporated into tools or systems where high-quality prompt generation is essential, such as:
+- AI writing assistants
+- Educational tools
+- Chatbots requiring specialized responses or tailored prompts
 
-<!-- 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
+Use the following peices of codes to start using the model:
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- PreRequisites
+```python
+!pip install -U bitsandbytes
+!pip install -U transformers
+```
 
-#### Speeds, Sizes, Times [optional]
+- Loading the model and its tokenizer
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+model = AutoModelForCausalLM.from_pretrained("abhinavsarkar/mistral-7b-instruct-v0.2-bb-4bit-finetuned-prompt-generator")
+tokenizer = AutoTokenizer.from_pretrained("abhinavsarkar/mistral-7b-instruct-v0.2-bb-4bit-finetuned-prompt-generator")
+```
 
-[More Information Needed]
+- Inferencing the model
+```python
+prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
 
-## Evaluation
+<|Instruction|>
+{}
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+|<Input|>
+{}
 
-### Testing Data, Factors & Metrics
+<|Response|>
+{}
+"""
 
-#### Testing Data
+input_text = "Your Input text"
 
-<!-- This should link to a Dataset Card if possible. -->
+inputs = tokenizer([
+    prompt.format(
+        "You are a prompt engineer. Your task is to craft a prompt based on the given input that ensures the model behaves exactly as described by the provided word.", # instruction
+        input_text, # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
 
-[More Information Needed]
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=512)
 
-#### Factors
+response = tokenizer.decode(output[0], skip_special_tokens=True)
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+start_token = "<|Response|>"
+end_token = "<|End|>"
 
-[More Information Needed]
+start_idx = response.find(start_token) + len(start_token)
+end_idx = response.find(end_token)
 
-#### Metrics
+final_response = response[start_idx:end_idx].strip()
+print(final_response)
+```
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Possible Errors and Solutions
 
-[More Information Needed]
+**Quantization Warnings**: 
+If you receive warnings about unused arguments or quantization settings, ensure you have `bitsandbytes` installed:
+```python
+!pip install -U bitsandbytes
+```
 
-### Results
+**Tokenizer Issues**:
+If you encounter tokenizer-related errors, update the `transformers` library:
+```python
+!pip install -U transformers
+```
 
-[More Information Needed]
+Restart the session after installing these packages.
 
-#### Summary
-
-
-
-## Model Examination [optional]
+## Training Details
 
-<!-- Relevant interpretability work for the model goes here -->
+### Training Data
 
-[More Information Needed]
+The model was fine-tuned on [fka/awesome-chatgpt-prompts], a curated dataset focused on general-purpose prompt generation, ensuring broad applicability across a wide range of topics and tasks.
 
-## Environmental Impact
+### Training Procedure
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+The model was fine-tuned using the Hugging Face Transformers library, Unsloth in a distributed environment(Google Collab, Kaggle), leveraging mixed-precision training for optimized performance.
 
-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).
+#### Training Hyperparameters
 
-- **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]
+- **Training regime:** fp16 mixed precision
+- **Epochs:** 30
+- **Batch size:** 2
+- **Gradient accumulation steps:** 4
+- **Learning rate:** 2e-4
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+This model is based on Mistral-7B architecture, optimized for efficient inference using 4-bit quantization and fine-tuned for the task of causal language modeling.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+The fine-tuning was conducted on a setup involving two T4 GPUs.
 
 #### 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]
+- **Framework**: PyTorch
+- **Libraries**: Hugging Face Transformers, Unsloth
 
-**APA:**
+## More Information
 
-[More Information Needed]
+For further details or inquiries, please reach out via [LinkedIn](https://www.linkedin.com/in/abhinavsarkarrr/) or email at [email protected].
 
-## Glossary [optional]
+## Model Card Authors
 
-<!-- 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]
+- Abhinav Sarkar
 
 ## Model Card Contact
 
-[More Information Needed]
+- [email protected]
+
+---