Text Generation
Transformers
Safetensors
Hungarian
llama
hungarian
causal-lm
sentencepiece
text-generation-inference
Instructions to use emese-tech/csermely with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emese-tech/csermely with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="emese-tech/csermely")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("emese-tech/csermely") model = AutoModelForCausalLM.from_pretrained("emese-tech/csermely") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use emese-tech/csermely with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "emese-tech/csermely" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emese-tech/csermely", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/emese-tech/csermely
- SGLang
How to use emese-tech/csermely 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 "emese-tech/csermely" \ --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": "emese-tech/csermely", "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 "emese-tech/csermely" \ --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": "emese-tech/csermely", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use emese-tech/csermely with Docker Model Runner:
docker model run hf.co/emese-tech/csermely
| { | |
| "architectures": ["LlamaForCausalLM"], | |
| "model_type": "llama", | |
| "model_version": "0.1", | |
| "vocab_size": 32000, | |
| "hidden_size": 768, | |
| "intermediate_size": 2048, | |
| "num_hidden_layers": 16, | |
| "num_attention_heads": 12, | |
| "num_key_value_heads": 12, | |
| "hidden_act": "silu", | |
| "max_position_embeddings": 8192, | |
| "rope_theta": 10000.0, | |
| "rope_scaling": { | |
| "type": "yarn", | |
| "factor": 4.0, | |
| "original_max_position_embeddings": 2048 | |
| }, | |
| "rms_norm_eps": 1e-5, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float16", | |
| "bos_token_id": 2, | |
| "eos_token_id": 3, | |
| "pad_token_id": 1, | |
| "transformers_version": "4.45.0" | |
| } | |