Update README.md

README.md

@@ -1,204 +1,142 @@
 ---
-base_model: meta-llama/Llama-2-7b-hf
 library_name: peft
+license: apache-2.0
+base_model: meta-llama/Llama-2-7b-hf
+tags:
+- resume-screening
+- hr-tech
+- llama2
+- lora
+- peft
+- fine-tuned
 ---
 
-# 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. -->
-
+# Advanced Resume Screening Model
 
+## Model Description
 
-- **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 is a LoRA (Low-Rank Adaptation) fine-tuned version of Llama-2-7B specifically optimized for resume screening and candidate evaluation tasks. The model can analyze resumes, extract key information, and provide structured assessments of candidate qualifications.
 
-### Model Sources [optional]
+- **Developed by:** kiritps
+- **Model type:** Causal Language Model (LoRA Fine-tuned)
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** meta-llama/Llama-2-7b-hf
 
-<!-- Provide the basic links for the model. -->
+## Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/kiritps/Advanced-resume-screening
 
 ## 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 model is designed for HR professionals and recruitment systems to:
+- Analyze and screen resumes automatically
+- Extract key qualifications and skills
+- Provide structured candidate assessments
+- Filter candidates based on specific criteria
+- Generate summaries of candidate profiles
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The model can be integrated into:
+- Applicant Tracking Systems (ATS)
+- HR management platforms
+- Recruitment automation tools
+- Candidate matching systems
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Should not be used as the sole decision-maker in hiring processes
+- Not intended for discriminatory screening based on protected characteristics
+- Not suitable for general-purpose text generation outside of resume/HR context
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Load base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
 
-## How to Get Started with the Model
+Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "kiritps/Advanced-resume-screening")
 
-Use the code below to get started with the model.
+Example usage
+prompt = "Analyze this resume and provide key qualifications: [RESUME TEXT HERE]"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512, temperature=0.7)
+response = tokenizer.decode(outputs, skip_special_tokens=True)
 
-[More Information Needed]
+text
 
 ## 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]
+The model was fine-tuned on a curated dataset of resume-response pairs, designed to teach the model how to:
+- Extract relevant information from resumes
+- Provide structured analysis of candidate qualifications
+- Generate appropriate screening responses
 
 ### 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. -->
+- **Training regime:** 4-bit quantization with bfloat16 mixed precision
+- **LoRA rank:** 64
+- **LoRA alpha:** 16
+- **Learning rate:** 2e-4
+- **Batch size:** 4
+- **Gradient accumulation steps:** 4
+- **Training epochs:** Multiple checkpoints saved (3840, 4320, 4800, 5280, 5760 steps)
 
-[More Information Needed]
+#### Quantization Configuration
 
-#### Metrics
+- **Quantization method:** bitsandbytes
+- **Load in 4bit:** True
+- **Quantization type:** nf4
+- **Double quantization:** True
+- **Compute dtype:** bfloat16
 
-<!-- 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]
+## Bias, Risks, and Limitations
 
-## Environmental Impact
+### Limitations
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+- Model responses should be reviewed by human recruiters
+- May exhibit biases present in training data
+- Performance may vary across different industries or job types
+- Requires careful prompt engineering for optimal results
 
-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).
+### Recommendations
 
-- **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]
+- Use as a screening aid, not a replacement for human judgment
+- Regularly audit outputs for potential bias
+- Combine with diverse evaluation methods
+- Ensure compliance with local employment laws and regulations
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+- **Parameter Count:** ~7B parameters (base) + LoRA adapters
+- **Quantization:** 4-bit NF4 quantization
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
-
-[More Information Needed]
+- GPU training environment
+- Compatible with consumer and enterprise GPUs
 
 #### Software
+- **Framework:** PyTorch
+- **PEFT Version:** 0.6.2
+- **Transformers:** Latest compatible version
+- **Quantization:** bitsandbytes
 
-[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]
-## Training procedure
-
+## Training Procedure
 
 The following `bitsandbytes` quantization config was used during training:
 - quant_method: bitsandbytes
@@ -212,7 +150,16 @@ The following `bitsandbytes` quantization config was used during training:
 - bnb_4bit_use_double_quant: True
 - bnb_4bit_compute_dtype: bfloat16
 
-### Framework versions
+### Framework Versions
+- PEFT 0.6.2
+- Transformers (compatible version)
+- PyTorch (latest stable)
+- bitsandbytes (for quantization)
+
+## Model Card Authors
 
+kiritps
 
-- PEFT 0.6.2
+## Model Card Contact
+
+For questions or issues regarding this model, please open an issue in the model repository.