Text Generation
Transformers
Safetensors
llama
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use Kendamarron/model-nemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kendamarron/model-nemo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kendamarron/model-nemo") 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("Kendamarron/model-nemo") model = AutoModelForCausalLM.from_pretrained("Kendamarron/model-nemo", 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 Kendamarron/model-nemo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kendamarron/model-nemo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kendamarron/model-nemo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Kendamarron/model-nemo
- SGLang
How to use Kendamarron/model-nemo 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 "Kendamarron/model-nemo" \ --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": "Kendamarron/model-nemo", "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 "Kendamarron/model-nemo" \ --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": "Kendamarron/model-nemo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Kendamarron/model-nemo with Docker Model Runner:
docker model run hf.co/Kendamarron/model-nemo
Download tokenizer_config.json from Kendamarron/model-nemo: direct link, hf CLI and curl.
- Browser
- Download file 2.58 kB
-
https://huggingface.co/Kendamarron/model-nemo/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Kendamarron/model-nemo/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Kendamarron/model-nemo/resolve/main/tokenizer_config.json
2.58 kB
| { | |
| "add_bos_token": true, | |
| "add_eos_token": false, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "<s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "</s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "3": { | |
| "content": "<MASK|LLM-jp>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "4": { | |
| "content": "<PAD|LLM-jp>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "5": { | |
| "content": "<CLS|LLM-jp>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "6": { | |
| "content": "<SEP|LLM-jp>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "7": { | |
| "content": "<EOD|LLM-jp>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "<s>", | |
| "chat_template": "{% set system_message = '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '### 指示:\n' + content + '\n\n### 応答:\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\n\n' }}{% endif %}{% endfor %}", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "<CLS|LLM-jp>", | |
| "eod_token": "</s>", | |
| "eos_token": "</s>", | |
| "extra_ids": 0, | |
| "mask_token": "<MASK|LLM-jp>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<PAD|LLM-jp>", | |
| "padding_side": "right", | |
| "sep_token": "<SEP|LLM-jp>", | |
| "sp_model_kwargs": {}, | |
| "split_special_tokens": false, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "<unk>" | |
| } | |