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

Libraries

How to use TaimoorSiddiqui/HopAlFikr with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TaimoorSiddiqui/HopAlFikr", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TaimoorSiddiqui/HopAlFikr with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TaimoorSiddiqui/HopAlFikr

SGLang

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

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

HopAlFikr

HopAlFikr is a fine-tuned language model optimized for conversational AI and coding assistance.

Model Details

Base Model: Mistral 7B v0.3
License: Apache 2.0 (Commercial use allowed)
Fine-tuning: LoRA
Languages: English, Urdu
Developer: TaimoorSiddiqui

Intended Use

Conversational AI assistant
Code generation and explanation
General knowledge Q&A
Educational applications

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TaimoorSiddiqui/HopAlFikr")
tokenizer = AutoTokenizer.from_pretrained("TaimoorSiddiqui/HopAlFikr")

inputs = tokenizer("Hello, how can I help you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Training

Framework: Transformers + PEFT (LoRA)
Hardware: Kaggle T4 GPU
Epochs: 3
Learning Rate: 2e-4

License

This model is licensed under the Apache 2.0 License.

You are free to:

✅ Use commercially
✅ Modify and distribute
✅ Use privately
✅ Patent use

Citation

@misc{hopalfikr2024,
  author = {Taimoor Siddiqui},
  title = {HopAlFikr: A Fine-tuned Language Model},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/TaimoorSiddiqui/HopAlFikr}
}

Acknowledgments

Built on the excellent Mistral 7B v0.3 base model.

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