Instructions to use FrontiersMind/Nandi-Mini-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrontiersMind/Nandi-Mini-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-150M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Nandi-Mini-150M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use FrontiersMind/Nandi-Mini-150M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FrontiersMind/Nandi-Mini-150M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrontiersMind/Nandi-Mini-150M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FrontiersMind/Nandi-Mini-150M
- SGLang
How to use FrontiersMind/Nandi-Mini-150M 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 "FrontiersMind/Nandi-Mini-150M" \ --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": "FrontiersMind/Nandi-Mini-150M", "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 "FrontiersMind/Nandi-Mini-150M" \ --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": "FrontiersMind/Nandi-Mini-150M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FrontiersMind/Nandi-Mini-150M with Docker Model Runner:
docker model run hf.co/FrontiersMind/Nandi-Mini-150M
Update tokenization_nandi.py
Browse files- tokenization_nandi.py +33 -27
tokenization_nandi.py
CHANGED
|
@@ -91,34 +91,40 @@ class NandiTokenizer(TokenizersBackend):
|
|
| 91 |
**kwargs,
|
| 92 |
)
|
| 93 |
|
| 94 |
-
def
|
| 95 |
-
|
| 96 |
-
text,
|
| 97 |
-
text_pair=None,
|
| 98 |
-
add_special_tokens: bool = True,
|
| 99 |
-
padding=False,
|
| 100 |
-
truncation=None,
|
| 101 |
-
max_length=None,
|
| 102 |
-
stride: int = 0,
|
| 103 |
-
padding_side=None,
|
| 104 |
-
return_tensors=None,
|
| 105 |
-
**kwargs,
|
| 106 |
-
):
|
| 107 |
if isinstance(text, str):
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 122 |
|
| 123 |
|
| 124 |
__all__ = ["NandiTokenizer"]
|
|
|
|
| 91 |
**kwargs,
|
| 92 |
)
|
| 93 |
|
| 94 |
+
def prepare_for_tokenization(self, text, **kwargs):
|
| 95 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 96 |
if isinstance(text, str):
|
| 97 |
+
text = "<|im_start|> " + text
|
| 98 |
+
return (text, kwargs)
|
| 99 |
+
|
| 100 |
+
# def encode(
|
| 101 |
+
# self,
|
| 102 |
+
# text,
|
| 103 |
+
# text_pair=None,
|
| 104 |
+
# add_special_tokens: bool = True,
|
| 105 |
+
# padding=False,
|
| 106 |
+
# truncation=None,
|
| 107 |
+
# max_length=None,
|
| 108 |
+
# stride: int = 0,
|
| 109 |
+
# padding_side=None,
|
| 110 |
+
# return_tensors=None,
|
| 111 |
+
# **kwargs,
|
| 112 |
+
# ):
|
| 113 |
+
# if isinstance(text, str):
|
| 114 |
+
# # This is a temporary fix to match the behaviour of the training pipeline
|
| 115 |
+
# text = "<|im_start|>" + " " + text
|
| 116 |
+
# return super().encode(
|
| 117 |
+
# text,
|
| 118 |
+
# text_pair=text_pair,
|
| 119 |
+
# add_special_tokens=add_special_tokens,
|
| 120 |
+
# padding=padding,
|
| 121 |
+
# truncation=truncation,
|
| 122 |
+
# max_length=max_length,
|
| 123 |
+
# stride=stride,
|
| 124 |
+
# padding_side=padding_side,
|
| 125 |
+
# return_tensors=return_tensors,
|
| 126 |
+
# **kwargs,
|
| 127 |
+
# )
|
| 128 |
|
| 129 |
|
| 130 |
__all__ = ["NandiTokenizer"]
|