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README.md

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 ---
-license: cc
-datasets:
-- databricks/databricks-dolly-15k
-- vicgalle/alpaca-gpt4
-pipeline_tag: text-generation
+library_name: peft
+base_model: meta-llama/Llama-2-7b-chat-hf
 ---
-# | NLP | LLM | Fine-tuning 2024 | Llama 2 QLoRA |
 
-## Natural Language Processing (NLP) and Large Language Models (LLM) with Fine-Tuning LLM Llama 2 with QLoRA in 2024
+# Model Card for Model ID
 
-![Learning](https://t3.ftcdn.net/jpg/06/14/01/52/360_F_614015247_EWZHvC6AAOsaIOepakhyJvMqUu5tpLfY.jpg)
+<!-- Provide a quick summary of what the model is/does. -->
 
-# <b><span style='color:#78D118'>|</span> Overview</b>
 
-In this notebook we're going to Fine-Tuning LLM:
 
-<img src="https://github.com/YanSte/NLP-LLM-Fine-tuning-Trainer/blob/main/img_2.png?raw=true" alt="Learning" width="50%">
+## Model Details
 
-Many LLMs are general purpose models trained on a broad range of data and use cases. This enables them to perform well in a variety of applications, as shown in previous modules. It is not uncommon though to find situations where applying a general purpose model performs unacceptably for specific dataset or use case. This often does not mean that the general purpose model is unusable. Perhaps, with some new data and additional training the model could be improved, or fine-tuned, such that it produces acceptable results for the specific use case.
+### Model Description
 
-<img src="https://github.com/YanSte/NLP-LLM-Fine-tuning-Trainer/blob/main/img_1.png?raw=true" alt="Learning" width="50%">
+<!-- Provide a longer summary of what this model is. -->
 
-Fine-tuning uses a pre-trained model as a base and continues to train it with a new, task targeted dataset. Conceptually, fine-tuning leverages that which has already been learned by a model and aims to focus its learnings further for a specific task.
 
-It is important to recognize that fine-tuning is model training. The training process remains a resource intensive, and time consuming effort. Albeit fine-tuning training time is greatly shortened as a result of having started from a pre-trained model. 
 
-<img src="https://github.com/YanSte/NLP-LLM-Fine-tuning-Trainer/blob/main/img_3.png?raw=true" alt="Learning" width="50%">
+- **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]
 
-[Kaggle](https://www.kaggle.com/yannicksteph/nlp-llm-fine-tuning-2024-llama-2-qlora/)
+### Model Sources [optional]
 
-## Learning Objectives
+<!-- Provide the basic links for the model. -->
 
-By the end of this notebook, you will gain expertise in the following areas:
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-1. Learn how to effectively prepare datasets for training.
-2. Few shots learning 
-3. Understand the process of fine-tuning the Llama 2 on QLoRA with SFTTrainer in 2024.
+## 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]
+
+
+### Framework versions
+
+- PEFT 0.7.1

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 8,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "down_proj",
+    "q_proj",
+    "o_proj",
+    "v_proj",
+    "k_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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