Instructions to use Mostafa8Mehrabi/qwen3-71M-c4-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mostafa8Mehrabi/qwen3-71M-c4-final with Transformers:

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

pipe = pipeline("text-generation", model="Mostafa8Mehrabi/qwen3-71M-c4-final")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-71M-c4-final")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-71M-c4-final")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mostafa8Mehrabi/qwen3-71M-c4-final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mostafa8Mehrabi/qwen3-71M-c4-final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mostafa8Mehrabi/qwen3-71M-c4-final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mostafa8Mehrabi/qwen3-71M-c4-final

SGLang

How to use Mostafa8Mehrabi/qwen3-71M-c4-final 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 "Mostafa8Mehrabi/qwen3-71M-c4-final" \
    --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": "Mostafa8Mehrabi/qwen3-71M-c4-final",
		"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 "Mostafa8Mehrabi/qwen3-71M-c4-final" \
        --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": "Mostafa8Mehrabi/qwen3-71M-c4-final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mostafa8Mehrabi/qwen3-71M-c4-final with Docker Model Runner:
```
docker model run hf.co/Mostafa8Mehrabi/qwen3-71M-c4-final
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🚀 Qwen3-50M C4 Pretrained (FP16) - Notebook Version

Pretrained Qwen3-50M model on C4 dataset using FP16 precision in notebook environment.

📊 Training Results

Final Training Loss: 4.0267
Final Validation Loss: 4.120617866516113
Training Samples: 1,000,000
Epochs: 3
Precision: FP16

🚀 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m-c4-final")
model = AutoModelForCausalLM.from_pretrained(
    "Mostafa8Mehrabi/qwen3-50m-c4-final", 
    torch_dtype=torch.float16,
    device_map="auto"
)

# Generate text
prompt = "The future of artificial intelligence is"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))