Instructions to use Sachin21112004/carrerflow-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Sachin21112004/carrerflow-ai with PEFT:
```
Task type is invalid.
```

How to use Sachin21112004/carrerflow-ai with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Sachin21112004/carrerflow-ai")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Sachin21112004/carrerflow-ai")
model = AutoModelForSequenceClassification.from_pretrained("Sachin21112004/carrerflow-ai")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sachin21112004/carrerflow-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sachin21112004/carrerflow-ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sachin21112004/carrerflow-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sachin21112004/carrerflow-ai

SGLang

How to use Sachin21112004/carrerflow-ai with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Sachin21112004/carrerflow-ai" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sachin21112004/carrerflow-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Sachin21112004/carrerflow-ai" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sachin21112004/carrerflow-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sachin21112004/carrerflow-ai with Docker Model Runner:
```
docker model run hf.co/Sachin21112004/carrerflow-ai
```

Model Card for CareerFlow-AI

Model Summary

CareerFlow-AI is a career-focused NLP model designed to understand, summarize, and reason over career guidance content, job descriptions, resumes, and skill-oriented text.
It is optimized for educational and professional career guidance use cases, covering school-level guidance (Class 1–12), higher education paths, and job-market intelligence.

Model Details

Model Description

CareerFlow-AI is a PEFT (LoRA)-based fine-tuned model built on DistilBERT, created to provide structured career intelligence.
The model understands career-related language such as roles, skills, qualifications, career paths, and job descriptions, and can be used in career advisory systems, dashboards, and AI assistants.

It is lightweight, fast, and suitable for real-world deployment where efficiency and interpretability are important.

Developed by: Sachin Rao
Funded by: Sachin Rao
Shared by: Sachin Rao
Model type: DistilBERT-based NLP model (PEFT / LoRA)
Language(s) (NLP): English
License: Other
Finetuned from model: DistilBERT

Model Sources

Repository: https://huggingface.co/Sachin21112004/carrerflow-ai
Paper: Not available
Demo: Not available

Uses

Direct Use

CareerFlow-AI can be directly used for:

Career guidance summarization
Understanding job descriptions and career text
Educational career advisory chatbots
Resume and skill-related content understanding
Career dashboards and analytics platforms

Downstream Use

The model can be integrated into:

Career recommendation engines
Student guidance portals
Job–skill matching systems
Resume analysis pipelines
Educational AI assistants

Out-of-Scope Use

Medical, legal, or financial advice
Autonomous hiring or rejection decisions
Real-time labor market prediction
High-stakes decision-making without human review

Bias, Risks, and Limitations

The model may reflect biases present in job market and career datasets
Certain domains (e.g., technology careers) may be over-represented
Career suggestions should not be treated as absolute recommendations
Performance may degrade for non-English or highly informal text

Recommendations

Users should apply human oversight when using the model in decision-support systems.
It is recommended to combine CareerFlow-AI outputs with domain expertise and fairness checks.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Sachin21112004/carrerflow-ai"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I want to become a software engineer and learn Python and DSA."
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)

Downloads last month: 1

Safetensors

Model size

67M params

Tensor type

F16

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