text-generation
Collection
3 items • Updated
How to use KurniaKadir/hrmy with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="KurniaKadir/hrmy") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KurniaKadir/hrmy", dtype="auto")How to use KurniaKadir/hrmy with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KurniaKadir/hrmy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "KurniaKadir/hrmy",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/KurniaKadir/hrmy
How to use KurniaKadir/hrmy with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "KurniaKadir/hrmy" \
--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": "KurniaKadir/hrmy",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "KurniaKadir/hrmy" \
--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": "KurniaKadir/hrmy",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use KurniaKadir/hrmy with Docker Model Runner:
docker model run hf.co/KurniaKadir/hrmy
Domain-Specific AI for Malaysian HR compliance with specialized capabilities in:
| Feature | Legal Basis | Accuracy |
|---|---|---|
| Gender Pay Gap Detection | Pay Equality Act 2024 | 92% |
| Ethnicity Variance Alerts | EA1955 Sec. 60L | 88% |
| Disability Pay Compliance | PDPA 2010 | 90% |
Example Output:
{
"analysis_type": "wage_disparity",
"results": {
"gender_gap": "18.2%",
"high_risk_roles": ["Senior Manager", "Operations Executive"],
"compliance_status": "⚠️ Requires HRD Corp review"
}
}
graph TD
A[Dispute Reported] --> B{Type?}
B -->|Unfair Dismissal| C[IRA1967 Sec. 20]
B -->|Harassment| D[POHA 2022]
C --> E[Generate Conciliation Proposal]
2025 Calculation Engine:
def calculate_epf(salary: float) -> dict:
rates = {
'employee': 0.11 if salary <= 5000 else 0.12,
'employer': 0.13 if salary <= 5000 else 0.12
}
return {k: v*salary for k,v in rates.items()}
Composition:
Bias Mitigation:
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Wage Gap Detection | MOHR Audit Cases | F1 | 0.91 |
| EPF Calculation | KWSP Test Samples | Accuracy | 99.2% |
| Malay Legal QA | MYCourt Bench | EM | 0.88 |
Transparency Measures:
Limitations:
from transformers import pipeline
hr_analyzer = pipeline(
"text-generation",
model="chemmara/MYHRA-2025",
trust_remote_code=True
)
# Wage disparity check
response = hr_analyzer("Analyze gender pay gap in Finance Department")
@model{myhra2025,
title = {Malaysian HR Assistant 2025},
author = {Chemmara Space Legal AI Team},
year = {2025},
version = {3.0.1},
url = {https://huggingface.co/chemmara/MYHRA-2025}
}
Base model
moonshotai/Kimi-K2-Instruct