Instructions to use MawaredHR/MawaredHR_Deepseek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MawaredHR/MawaredHR_Deepseek with Transformers:

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

pipe = pipeline("text-generation", model="MawaredHR/MawaredHR_Deepseek")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MawaredHR/MawaredHR_Deepseek")
model = AutoModelForCausalLM.from_pretrained("MawaredHR/MawaredHR_Deepseek")
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

vLLM

How to use MawaredHR/MawaredHR_Deepseek with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MawaredHR/MawaredHR_Deepseek

SGLang

How to use MawaredHR/MawaredHR_Deepseek 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 "MawaredHR/MawaredHR_Deepseek" \
    --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": "MawaredHR/MawaredHR_Deepseek",
		"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 "MawaredHR/MawaredHR_Deepseek" \
        --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": "MawaredHR/MawaredHR_Deepseek",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use MawaredHR/MawaredHR_Deepseek with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MawaredHR/MawaredHR_Deepseek to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MawaredHR/MawaredHR_Deepseek to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MawaredHR/MawaredHR_Deepseek to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MawaredHR/MawaredHR_Deepseek",
    max_seq_length=2048,
)

Docker Model Runner
How to use MawaredHR/MawaredHR_Deepseek with Docker Model Runner:
```
docker model run hf.co/MawaredHR/MawaredHR_Deepseek
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MaWared HR Reasoning Model

Model Details

Base Model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit
Finetuned by: Daemontatox
License: Apache-2.0
Language: English
Tags: text-generation-inference, transformers, unsloth, qwen2, trl

Overview

This model is a finetuned version of the deepseek-r1-distill-qwen-7b model, optimized for MaWared HR reasoning. It was trained using Unsloth and Hugging Face's TRL library, enabling 2x faster training performance.

Features

HR Query Reasoning: Provides logical and well-structured responses to complex HR-related inquiries.
Decision Support: Assists HR professionals in making informed decisions based on policies and regulations.
Enhanced Performance: Optimized for deep reasoning and contextual understanding in HR-related scenarios.

Installation

To use this model, install the required dependencies:

pip install torch transformers accelerate unsloth

Usage

You can load and use the model with the following Python snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Daemontatox/mawared-hr-reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

input_text = "How should I handle a conflict between employees?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_length=100)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Acknowledgments

This model was developed using Unsloth and Hugging Face's TRL library. Special thanks to the open-source community for their contributions.

License This model is licensed under the Apache-2.0 license.

vbnet


Let me know if you need any modifications! 🚀

Downloads last month: 2

Model tree for MawaredHR/MawaredHR_Deepseek

Quantizations

3 models