Text Generation
Transformers
Safetensors
Korean
English
llama
korean
instruction-tuning
lora
merged
bilingual
visionspace
unsloth
conversational
text-generation-inference
Instructions to use MyeongHo0621/eeve-vss-smh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MyeongHo0621/eeve-vss-smh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MyeongHo0621/eeve-vss-smh") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MyeongHo0621/eeve-vss-smh") model = AutoModelForCausalLM.from_pretrained("MyeongHo0621/eeve-vss-smh", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MyeongHo0621/eeve-vss-smh with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MyeongHo0621/eeve-vss-smh" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MyeongHo0621/eeve-vss-smh", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MyeongHo0621/eeve-vss-smh
- SGLang
How to use MyeongHo0621/eeve-vss-smh 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 "MyeongHo0621/eeve-vss-smh" \ --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": "MyeongHo0621/eeve-vss-smh", "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 "MyeongHo0621/eeve-vss-smh" \ --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": "MyeongHo0621/eeve-vss-smh", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use MyeongHo0621/eeve-vss-smh with Docker Model Runner:
docker model run hf.co/MyeongHo0621/eeve-vss-smh
Upload tokenizer from checkpoint-6250 (Trained with Unsloth)
Browse files- tokenizer.json +1 -6
- tokenizer_config.json +2 -5
tokenizer.json
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"direction": "Right",
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"max_length": 2048,
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"strategy": "LongestFirst",
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"stride": 0
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"padding": null,
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"added_tokens": [
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tokenizer_config.json
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"eos_token": "<|im_end|>",
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"extra_special_tokens": {},
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"legacy": true,
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"
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"stride": 0,
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"tokenizer_class": "LlamaTokenizerFast",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"trust_remote_code": false,
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"unk_token": "<unk>",
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"use_default_system_prompt": false,
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"eos_token": "<|im_end|>",
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"extra_special_tokens": {},
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"legacy": true,
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"model_max_length": 4096,
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"pad_token": "</s>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizerFast",
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"trust_remote_code": false,
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"unk_token": "<unk>",
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"use_default_system_prompt": false,
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