Instructions to use Omarrran/Qwen2_5_7B_hnm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Omarrran/Qwen2_5_7B_hnm with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("Omarrran/Qwen2_5_7B_hnm", set_active=True)

Transformers

How to use Omarrran/Qwen2_5_7B_hnm with Transformers:

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

pipe = pipeline("text-generation", model="Omarrran/Qwen2_5_7B_hnm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Omarrran/Qwen2_5_7B_hnm")
model = AutoModelForCausalLM.from_pretrained("Omarrran/Qwen2_5_7B_hnm")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Omarrran/Qwen2_5_7B_hnm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Omarrran/Qwen2_5_7B_hnm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omarrran/Qwen2_5_7B_hnm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Omarrran/Qwen2_5_7B_hnm

SGLang

How to use Omarrran/Qwen2_5_7B_hnm 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 "Omarrran/Qwen2_5_7B_hnm" \
    --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": "Omarrran/Qwen2_5_7B_hnm",
		"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 "Omarrran/Qwen2_5_7B_hnm" \
        --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": "Omarrran/Qwen2_5_7B_hnm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Omarrran/Qwen2_5_7B_hnm 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 Omarrran/Qwen2_5_7B_hnm 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 Omarrran/Qwen2_5_7B_hnm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Omarrran/Qwen2_5_7B_hnm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Omarrran/Qwen2_5_7B_hnm",
    max_seq_length=2048,
)

Docker Model Runner
How to use Omarrran/Qwen2_5_7B_hnm with Docker Model Runner:
```
docker model run hf.co/Omarrran/Qwen2_5_7B_hnm
```

Uploaded model

Developed by: Haq Nawa Malik
License: apache-2.0

Key Features

Unsloth: Leverages Unsloth for efficient and faster fine-tuning.
4-bit Quantization: Utilizes 4-bit quantization to reduce memory usage and improve performance.
LoRA Adapters: Employs LoRA adapters to update only a small percentage of parameters.
Hugging Face TRL: Uses the SFTTrainer from TRL for supervised fine-tuning.
Alpaca Dataset: Trains on the yahma/alpaca-cleaned dataset.

Model Card Details

Model Name: Omarrran/Qwen2_5_7B_hnm

Dataset: yahma/alpaca-cleaned

Training Method: Supervised Fine-Tuning with LoRA adapters.

Quantization: 4-bit quantization.

Training Steps: 90 steps (adjust for full training runs).

LoRA Parameters: r=16, lora_alpha=16, lora_dropout=0, bias="none".

Maximum Sequence Length: 2048 tokens

Downloads last month: -

Omarrran
/

Qwen2_5_7B_hnm

Uploaded model

Key Features

Model Card Details

Dataset used to train Omarrran/Qwen2_5_7B_hnm