Instructions to use keerthanshetty/resume-skill-extractor-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keerthanshetty/resume-skill-extractor-merged with Transformers:

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

pipe = pipeline("text-generation", model="keerthanshetty/resume-skill-extractor-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("keerthanshetty/resume-skill-extractor-merged")
model = AutoModelForCausalLM.from_pretrained("keerthanshetty/resume-skill-extractor-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keerthanshetty/resume-skill-extractor-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keerthanshetty/resume-skill-extractor-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keerthanshetty/resume-skill-extractor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/keerthanshetty/resume-skill-extractor-merged

SGLang

How to use keerthanshetty/resume-skill-extractor-merged 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 "keerthanshetty/resume-skill-extractor-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keerthanshetty/resume-skill-extractor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "keerthanshetty/resume-skill-extractor-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keerthanshetty/resume-skill-extractor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use keerthanshetty/resume-skill-extractor-merged with Docker Model Runner:
```
docker model run hf.co/keerthanshetty/resume-skill-extractor-merged
```

🚀 Resume Skill Extractor (Llama 3 8B)

Model Overview

This is a fully merged, standalone 8-Billion parameter language model specifically fine-tuned to act as an expert technical recruiter. It takes raw, unstructured job descriptions and instantly extracts a concise summary along with a cleanly formatted, bulleted list of mandatory and bonus technical skills.

This model was created by fine-tuning Meta-Llama-3-8B-Instruct using QLoRA, and the resulting adapters have been permanently merged into the base weights for simplified deployment and serverless inference.

Base Model: Meta-Llama-3-8B-Instruct
Original LoRA Adapters: k10shetty/resume-skill-extractor-lora
Task: Information Extraction / Text Generation
Architecture: LlamaForCausalLM (16-bit bfloat16)

Prompt Format

This model retains the standard Llama 3 Instruct prompt format. To get the best results, you must use this exact structure:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are an expert technical recruiter. Analyze job descriptions and extract key information.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Analyze this job description and provide a summary, the required skills, and the bonus/nice-to-have skills:

[INSERT JOB DESCRIPTION HERE]<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for keerthanshetty/resume-skill-extractor-merged

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

(1118)

this model

keerthanshetty
/

resume-skill-extractor-merged

🚀 Resume Skill Extractor (Llama 3 8B)

Model Overview

Prompt Format

Model tree for keerthanshetty/resume-skill-extractor-merged

Dataset used to train keerthanshetty/resume-skill-extractor-merged