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README.md

@@ -3,200 +3,82 @@ base_model: akjindal53244/Llama-3.1-Storm-8B
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# LoRA fine-tuned Llama-3.1-Storm-8B on CV/resume and job description matching task
+![Alt text](LLMF.png)
 
 
 ## 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
+Tired of sifting through endless resumes and job descriptions? Meet the newest AI talent matchmaker, crafted with [LlamaFactory.AI]! This model delivers comprehensive candidate evaluations by analyzing the intricate relationships between resumes and job requirements. The model processes detailed instructions alongside CV and job description inputs to generate thorough matching analyses, complete with quantitative scores and specific recommendations. Going beyond traditional keyword matching, it evaluates candidate qualifications in context, providing structured insights that help streamline the recruitment process. This tool represents a significant step forward in bringing objective, consistent, and scalable candidate assessment to HR professionals and hiring managers. Developed to enhance recruitment efficiency while maintaining high accuracy in candidate evaluation, this model demonstrates the practical application of AI in solving real-world hiring challenges
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+- **Developed by:** [llamafactory.ai](https://llamafactory.ai/)
+- **Model type:** text generation
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B)
 
-### 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]
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-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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]
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-## 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. -->
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-[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. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## 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]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
-
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+base_model_name = "akjindal53244/Llama-3.1-Storm-8B"
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Load the LoRA adapter
+peft_model_id = "LlamaFactoryAI/cv-job-description-matching"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# Use the model
+messages = [
+    {
+        "role": "system",
+        "content": """You are an advanced AI model designed to analyze the compatibility between a CV and a job description. You will receive a CV and a job description. Your task is to output a structured JSON format that includes the following:
+
+1. matching_analysis: Analyze the CV against the job description to identify key strengths and gaps.
+2. description: Summarize the relevance of the CV to the job description in a few concise sentences.
+3. score: Provide a numerical compatibility score (0-100) based on qualifications, skills, and experience.
+4. recommendation: Suggest actions for the candidate to improve their match or readiness for the role.
+
+Your output must be in JSON format as follows:
+{
+  "matching_analysis": "Your detailed analysis here.",
+  "description": "A brief summary here.",
+  "score": 85,
+  "recommendation": "Your suggestions here."
+}
+""",
+    },
+    {"role": "user", "content": "<CV> {cv} </CV>\n<job_description> {job_description} </job_description>"},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+)
+outputs = model.generate(inputs, max_new_tokens=128)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+
+
+## Model Card Authors 
+
+[@xbeket](https://huggingface.co/xbeket)
 ## Model Card Contact
 
-[More Information Needed]
+[Discord](https://discord.com/invite/TrPmq2GT2V)
 ### Framework versions
 
 - PEFT 0.12.0