KissanAI
/

Dhenu2-In-Llama3.2-3B-Instruct

@@ -1,200 +1,64 @@
 ---
 library_name: transformers
 tags:
-- llama-factory
 ---
-# Model Card for Model ID
-<!-- 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]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## 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]
 ## 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]
-<!-- 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]

 ---
 library_name: transformers
 tags:
+- llama3.2 3B
 ---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/616d9181c3bac80637586601/Hca3EYzBdZZhRQwzmyZnY.png)
+# Dhenu2 India 3B
+## Model Overview
+**Model Name:** Llama3.2-Dhenu2-In-3B-Instruct
+**Architecture:** Llama3.2
+**Parameters:** 3 Billion
+**Release Date:** 24th October, 2024
+**License:** [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+## Description
+Dhenu2 India 3B offers a balanced performance, making it versatile for a wide range of agricultural applications. Built on the Llama3.2 architecture, this model is optimized to provide reliable conversational capabilities, ensuring a harmonious blend of knowledge depth and responsiveness. It is ideal for conversational applications that require accurate information and interactive engagement in the agricultural domain.
+## Intended Use
+- **Chatbots and Virtual Assistants:** Develop interactive tools that assist farmers with daily agricultural queries and tasks.
+## Training Data
+Dhenu2 India 3B was trained on a curated dataset that includes:
+- **Instruction Set:** Over 1.5 million instructions from real and synthetic conversations.
+- **Synthetic Instructions:** Generated to encompass more than 4,000 diverse agricultural topics.
+- **Data Sources:** Mobile extension service logs, farmer feedback, agricultural package of practices, and localized studies.
+## Training Procedure
+- **Techniques:** Utilized a combination of full fine-tuning and Low-Rank Adaptation (LoRA) to enhance model performance while conserving computational resources.
+- **Hardware:** Trained on multi-GPU configurations with NVIDIA A100 GPUs, employing DeepSpeed for efficient distributed training.
+- **Optimization:** Applied flash attention mechanisms to improve computational efficiency and reduce memory usage.
 ## Evaluation
+- **Human Evaluation:** Reviewed by agricultural experts for the accuracy and relevance of responses.
+- **Synthetic Evaluation:** Conducted peer assessments using other LLMs to verify consistency and correctness.
+- **Performance Metrics:** Measured based on domain-specific accuracy, response relevancy, and conversational fluency.
+## Limitations
+Dhenu2 India 3B is tailored for agricultural applications and may not perform optimally outside this domain. It is essential to deploy this model within relevant agricultural contexts to maintain the accuracy and reliability of its responses.
+## API
+Use our platform [Dhenu](https://dhenu.ai) with a generous free quota to start building your agriculture applications.
+## A note of gratitude
+We want to thank our partners Microsoft and Microsoft for Startups for landing us compute. We would also like to thank our partner, Meta, for the open-source Llama models.
+## Contact
+For more information, support, or collaboration inquiries, please contact us at [support@kissan.ai].