Text Generation
Transformers
TensorBoard
Safetensors
gemma3_text
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use devneko/MyGemmaNPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devneko/MyGemmaNPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="devneko/MyGemmaNPC") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("devneko/MyGemmaNPC") model = AutoModelForCausalLM.from_pretrained("devneko/MyGemmaNPC") 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 devneko/MyGemmaNPC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "devneko/MyGemmaNPC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "devneko/MyGemmaNPC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/devneko/MyGemmaNPC
- SGLang
How to use devneko/MyGemmaNPC 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 "devneko/MyGemmaNPC" \ --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": "devneko/MyGemmaNPC", "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 "devneko/MyGemmaNPC" \ --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": "devneko/MyGemmaNPC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use devneko/MyGemmaNPC with Docker Model Runner:
docker model run hf.co/devneko/MyGemmaNPC
Model save
Browse files
generation_config.json
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"cache_implementation": "hybrid",
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"do_sample": true,
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"eos_token_id": [
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "4.55.0"
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}
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model.safetensors
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runs/Aug15_03-06-53_9c10db51bdda/events.out.tfevents.1755227217.9c10db51bdda.7631.0
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