Text Generation
Transformers
TensorBoard
Safetensors
gemma3_text
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use TanishkB/RandomNumberGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TanishkB/RandomNumberGenerator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TanishkB/RandomNumberGenerator") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TanishkB/RandomNumberGenerator") model = AutoModelForCausalLM.from_pretrained("TanishkB/RandomNumberGenerator", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TanishkB/RandomNumberGenerator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TanishkB/RandomNumberGenerator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TanishkB/RandomNumberGenerator", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TanishkB/RandomNumberGenerator
- SGLang
How to use TanishkB/RandomNumberGenerator 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 "TanishkB/RandomNumberGenerator" \ --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": "TanishkB/RandomNumberGenerator", "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 "TanishkB/RandomNumberGenerator" \ --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": "TanishkB/RandomNumberGenerator", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use TanishkB/RandomNumberGenerator with Docker Model Runner:
docker model run hf.co/TanishkB/RandomNumberGenerator
Update README.md
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README.md
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```python
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from transformers import pipeline
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generator = pipeline(
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```
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## Training procedure
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```python
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from transformers import pipeline
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# Load text-generation pipeline
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generator = pipeline(
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"text-generation",
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model="TanishkB/RandomNumber",
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device=-1 # use 0 if you have GPU
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)
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print("Chat with it — type 'exit' to quit.")
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while True:
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user_input = input(">> ").strip()
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if user_input.lower() in ("exit", "quit"):
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break
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# Build single-turn prompt (no history)
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prompt = f"User: {user_input}\nAssistant:"
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# Generate reply
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response = generator(
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prompt,
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max_new_tokens=128,
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return_full_text=False
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)[0]["generated_text"]
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# Clean up model output (remove repeated labels if any)
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reply = response.strip()
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if reply.lower().startswith("assistant:"):
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reply = reply[len("assistant:"):].strip()
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print(reply)
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```
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## Training procedure
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