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.ipynb_checkpoints/README-checkpoint.md

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+---
+license: apache-2.0
+tags:
+  - chat
+  - chatbot
+  - LoRA
+  - instruction-tuning
+  - conversational
+  - tinyllama
+  - transformers
+language:
+  - en
+datasets:
+  - tatsu-lab/alpaca
+  - databricks/databricks-dolly-15k
+  - knkarthick/dialogsum
+  - Anthropic/hh-rlhf
+  - OpenAssistant/oasst1
+  - nomic-ai/gpt4all_prompt_generations
+  - sahil2801/CodeAlpaca-20k
+  - Open-Orca/OpenOrca
+model-index:
+  - name: chatbot-v2
+    results: []
+---
+
+# 🤖 chatbot-v2 — TinyLLaMA Instruction-Tuned Chatbot (LoRA)
+
+`chatbot-v2` is a lightweight, instruction-following conversational AI model based on **TinyLLaMA** and fine-tuned using **LoRA** adapters. It has been trained on a carefully curated mixture of open datasets covering assistant-like responses, code generation, summarization, safety alignment, and dialog reasoning.
+
+This model is ideal for embedding into mobile or edge apps with low-resource inference needs or running via an API.
+
+---
+
+## 🧠 Base Model
+
+- **Model**: [`TinyLlama/TinyLlama-1.1B-Chat`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat)
+- **Architecture**: Decoder-only Transformer (GPT-style)
+- **Fine-tuning method**: LoRA (low-rank adapters)
+- **LoRA Parameters**:
+  - `r=16`
+  - `alpha=32`
+  - `dropout=0.05`
+  - Target modules: `q_proj`, `v_proj`
+
+---
+
+## 📚 Training Datasets
+
+The model was fine-tuned on the following instruction-following, summarization, and dialogue datasets:
+
+- [`tatsu-lab/alpaca`](https://huggingface.co/datasets/tatsu-lab/alpaca) — Stanford Alpaca dataset
+- [`databricks/databricks-dolly-15k`](https://huggingface.co/datasets/databricks/databricks-dolly-15k) — Dolly instruction data
+- [`knkarthick/dialogsum`](https://huggingface.co/datasets/knkarthick/dialogsum) — Summarization of dialogs
+- [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) — Harmless/helpful/honest alignment data
+- [`OpenAssistant/oasst1`](https://huggingface.co/datasets/OpenAssistant/oasst1) — OpenAssistant dialogues
+- [`nomic-ai/gpt4all_prompt_generations`](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations) — Instructional prompt-response pairs
+- [`sahil2801/CodeAlpaca-20k`](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) — Programming/code generation instructions
+- [`Open-Orca/OpenOrca`](https://huggingface.co/datasets/Open-Orca/OpenOrca) — High-quality responses to complex questions
+
+---
+
+## 🔧 Intended Use
+
+This model is best suited for:
+
+- **Conversational agents / chatbots**
+- **Instruction-following assistants**
+- **Lightweight AI on edge devices (via server inference)**
+- **Educational tools and experiments**
+
+---
+
+## 🚫 Limitations
+
+- This model is **not suitable for production use** without safety reviews.
+- It may generate **inaccurate or biased responses**, as training data is from public sources.
+- It is **not safe for sensitive or medical domains**.
+
+---
+
+## 💬 Example Prompt
+
+Instruction:
+
+Explain the difference between supervised and unsupervised learning.
+
+Response:
+
+Supervised learning uses labeled data to train models, while unsupervised learning uses unlabeled data to discover patterns or groupings in the data…
+
+---
+
+## 📥 How to Load the Adapters
+
+To use this model, load the base TinyLLaMA model and apply the LoRA adapters:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "TinyLlama/TinyLlama-1.1B-Chat",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat")
+
+model = PeftModel.from_pretrained(base_model, "sahil239/chatbot-v2")
+
+📄 License
+
+This model is distributed under the Apache 2.0 License.
+
+🙏 Acknowledgements
+
+Thanks to the open-source datasets and projects: Alpaca, Dolly, OpenAssistant, Anthropic, OpenOrca, CodeAlpaca, GPT4All, and Hugging Face.    

README.md

@@ -1,207 +1,117 @@
 ---
-base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-library_name: peft
-pipeline_tag: text-generation
+license: apache-2.0
 tags:
-- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
-- lora
-- transformers
+  - chat
+  - chatbot
+  - LoRA
+  - instruction-tuning
+  - conversational
+  - tinyllama
+  - transformers
+language:
+  - en
+datasets:
+  - tatsu-lab/alpaca
+  - databricks/databricks-dolly-15k
+  - knkarthick/dialogsum
+  - Anthropic/hh-rlhf
+  - OpenAssistant/oasst1
+  - nomic-ai/gpt4all_prompt_generations
+  - sahil2801/CodeAlpaca-20k
+  - Open-Orca/OpenOrca
+model-index:
+  - name: chatbot-v2
+    results: []
 ---
 
-# Model Card for Model ID
+# 🤖 chatbot-v2 — TinyLLaMA Instruction-Tuned Chatbot (LoRA)
 
-<!-- Provide a quick summary of what the model is/does. -->
+`chatbot-v2` is a lightweight, instruction-following conversational AI model based on **TinyLLaMA** and fine-tuned using **LoRA** adapters. It has been trained on a carefully curated mixture of open datasets covering assistant-like responses, code generation, summarization, safety alignment, and dialog reasoning.
 
+This model is ideal for embedding into mobile or edge apps with low-resource inference needs or running via an API.
 
+---
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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
+## 🧠 Base Model
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+- **Model**: [`TinyLlama/TinyLlama-1.1B-Chat`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat)
+- **Architecture**: Decoder-only Transformer (GPT-style)
+- **Fine-tuning method**: LoRA (low-rank adapters)
+- **LoRA Parameters**:
+  - `r=16`
+  - `alpha=32`
+  - `dropout=0.05`
+  - Target modules: `q_proj`, `v_proj`
 
-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]
+## 📚 Training Datasets
 
-## Technical Specifications [optional]
+The model was fine-tuned on the following instruction-following, summarization, and dialogue datasets:
 
-### Model Architecture and Objective
+- [`tatsu-lab/alpaca`](https://huggingface.co/datasets/tatsu-lab/alpaca) — Stanford Alpaca dataset
+- [`databricks/databricks-dolly-15k`](https://huggingface.co/datasets/databricks/databricks-dolly-15k) — Dolly instruction data
+- [`knkarthick/dialogsum`](https://huggingface.co/datasets/knkarthick/dialogsum) — Summarization of dialogs
+- [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) — Harmless/helpful/honest alignment data
+- [`OpenAssistant/oasst1`](https://huggingface.co/datasets/OpenAssistant/oasst1) — OpenAssistant dialogues
+- [`nomic-ai/gpt4all_prompt_generations`](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations) — Instructional prompt-response pairs
+- [`sahil2801/CodeAlpaca-20k`](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) — Programming/code generation instructions
+- [`Open-Orca/OpenOrca`](https://huggingface.co/datasets/Open-Orca/OpenOrca) — High-quality responses to complex questions
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
+## 🔧 Intended Use
 
-[More Information Needed]
+This model is best suited for:
 
-#### Hardware
+- **Conversational agents / chatbots**
+- **Instruction-following assistants**
+- **Lightweight AI on edge devices (via server inference)**
+- **Educational tools and experiments**
 
-[More Information Needed]
+---
 
-#### Software
+## 🚫 Limitations
 
-[More Information Needed]
+- This model is **not suitable for production use** without safety reviews.
+- It may generate **inaccurate or biased responses**, as training data is from public sources.
+- It is **not safe for sensitive or medical domains**.
 
-## 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. -->
+## 💬 Example Prompt
 
-**BibTeX:**
+Instruction:
 
-[More Information Needed]
+Explain the difference between supervised and unsupervised learning.
 
-**APA:**
+Response:
 
-[More Information Needed]
+Supervised learning uses labeled data to train models, while unsupervised learning uses unlabeled data to discover patterns or groupings in the data…
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## 📥 How to Load the Adapters
 
-[More Information Needed]
+To use this model, load the base TinyLLaMA model and apply the LoRA adapters:
 
-## More Information [optional]
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+base_model = AutoModelForCausalLM.from_pretrained(
+    "TinyLlama/TinyLlama-1.1B-Chat",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat")
 
-## Model Card Authors [optional]
+model = PeftModel.from_pretrained(base_model, "sahil239/chatbot-v2")
 
-[More Information Needed]
+📄 License
 
-## Model Card Contact
+This model is distributed under the Apache 2.0 License.
 
-[More Information Needed]
-### Framework versions
+🙏 Acknowledgements
 
-- PEFT 0.17.0
+Thanks to the open-source datasets and projects: Alpaca, Dolly, OpenAssistant, Anthropic, OpenOrca, CodeAlpaca, GPT4All, and Hugging Face.