Instructions to use saucam/Navrasa-2.0-Rudra-7b-dare-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saucam/Navrasa-2.0-Rudra-7b-dare-ties with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="saucam/Navrasa-2.0-Rudra-7b-dare-ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saucam/Navrasa-2.0-Rudra-7b-dare-ties")
model = AutoModelForCausalLM.from_pretrained("saucam/Navrasa-2.0-Rudra-7b-dare-ties")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use saucam/Navrasa-2.0-Rudra-7b-dare-ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saucam/Navrasa-2.0-Rudra-7b-dare-ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saucam/Navrasa-2.0-Rudra-7b-dare-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/saucam/Navrasa-2.0-Rudra-7b-dare-ties

SGLang

How to use saucam/Navrasa-2.0-Rudra-7b-dare-ties 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 "saucam/Navrasa-2.0-Rudra-7b-dare-ties" \
    --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": "saucam/Navrasa-2.0-Rudra-7b-dare-ties",
		"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 "saucam/Navrasa-2.0-Rudra-7b-dare-ties" \
        --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": "saucam/Navrasa-2.0-Rudra-7b-dare-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use saucam/Navrasa-2.0-Rudra-7b-dare-ties with Docker Model Runner:
```
docker model run hf.co/saucam/Navrasa-2.0-Rudra-7b-dare-ties
```

Navrasa-2.0-Rudra-7b-dare-ties / tokenizer_config.json

saucam

Upload folder using huggingface_hub

c64c37c verified about 2 years ago

raw

history blame contribute delete

1.11 kB

	{
	"add_bos_token": true,
	"add_eos_token": false,
	"added_tokens_decoder": {
	"0": {
	"content": "<pad>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"1": {
	"content": "<eos>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"2": {
	"content": "<bos>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"3": {
	"content": "<unk>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"bos_token": "<bos>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<eos>",
	"legacy": null,
	"model_max_length": 8192,
	"pad_token": "<pad>",
	"padding_side": "right",
	"sp_model_kwargs": {},
	"spaces_between_special_tokens": false,
	"tokenizer_class": "GemmaTokenizer",
	"unk_token": "<unk>",
	"use_default_system_prompt": false
	}