Instructions to use Data4GoodCenter/careermatch-llama3-8b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Data4GoodCenter/careermatch-llama3-8b-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Data4GoodCenter/careermatch-llama3-8b-lora")

Transformers

How to use Data4GoodCenter/careermatch-llama3-8b-lora with Transformers:

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

pipe = pipeline("text-generation", model="Data4GoodCenter/careermatch-llama3-8b-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Data4GoodCenter/careermatch-llama3-8b-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Data4GoodCenter/careermatch-llama3-8b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Data4GoodCenter/careermatch-llama3-8b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Data4GoodCenter/careermatch-llama3-8b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Data4GoodCenter/careermatch-llama3-8b-lora

SGLang

How to use Data4GoodCenter/careermatch-llama3-8b-lora 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 "Data4GoodCenter/careermatch-llama3-8b-lora" \
    --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": "Data4GoodCenter/careermatch-llama3-8b-lora",
		"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 "Data4GoodCenter/careermatch-llama3-8b-lora" \
        --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": "Data4GoodCenter/careermatch-llama3-8b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Data4GoodCenter/careermatch-llama3-8b-lora with Docker Model Runner:
```
docker model run hf.co/Data4GoodCenter/careermatch-llama3-8b-lora
```

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Model Card for Model ID

Model Details

This model is a LoRA fine-tuned version of Meta LLaMA 3.1 8B Instruct (4-bit quantized) using PEFT. It is optimized for the downstream task of extracting skills from resumes and efficient inference using low-rank adaptation. The model was trained using Unsloth for efficient fine-tuning, enabling faster training with reduced VRAM usage.

Model Description

Developed by: Dat4Good Centre
Funded by [optional]: [More Information Needed]
Shared by [optional]: Sudharsana Kannan
Model type: Causal Language Model (Instruction-tuned with LoRA adapters)
Language(s) (NLP): English
License: [More Information Needed]
Finetuned from model [optional]: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit

Model Sources [optional]

Repository: https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

The model is designed for extracting structured skills, competencies, and technical attributes from resumes. It can convert unstructured resume text into skill representations for downstream parsing, analytics, or matching systems

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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Glossary [optional]

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Model Card Contact

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Framework versions

PEFT 0.18.1

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Paper for Data4GoodCenter/careermatch-llama3-8b-lora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61