Instructions to use FrontiersMind/Nandi-Mini-600M-Early-Checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Nandi-Mini-600M-Early-Checkpoint with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-600M-Early-Checkpoint", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Nandi-Mini-600M-Early-Checkpoint", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrontiersMind/Nandi-Mini-600M-Early-Checkpoint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Nandi-Mini-600M-Early-Checkpoint"
# 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-600M-Early-Checkpoint",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FrontiersMind/Nandi-Mini-600M-Early-Checkpoint

SGLang

How to use FrontiersMind/Nandi-Mini-600M-Early-Checkpoint 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-600M-Early-Checkpoint" \
    --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-600M-Early-Checkpoint",
		"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-600M-Early-Checkpoint" \
        --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-600M-Early-Checkpoint",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FrontiersMind/Nandi-Mini-600M-Early-Checkpoint with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Nandi-Mini-600M-Early-Checkpoint
```

vishesh-t27 commited on 7 days ago

Commit

b118bcb

verified ·

1 Parent(s): 5a65c7c

Update tokenization_nandi.py

Browse files

Files changed (1) hide show

tokenization_nandi.py +28 -28

tokenization_nandi.py CHANGED Viewed

@@ -110,34 +110,34 @@ class NandiTokenizer(TokenizersBackend):
-    # def encode(
-    #     self,
-    #     text,
-    #     text_pair=None,
-    #     add_special_tokens: bool = True,
-    #     padding=False,
-    #     truncation=None,
-    #     max_length=None,
-    #     stride: int = 0,
-    #     padding_side=None,
-    #     return_tensors=None,
-    #     **kwargs,
-    # ):
-    #     if isinstance(text, str):
-    #         # This is a temporary fix to match the behaviour of the training pipeline
-    #         text = "<|im_start|>" + " " + text
-    #     return super().encode(
-    #         text,
-    #         text_pair=text_pair,
-    #         add_special_tokens=add_special_tokens,
-    #         padding=padding,
-    #         truncation=truncation,
-    #         max_length=max_length,
-    #         stride=stride,
-    #         padding_side=padding_side,
-    #         return_tensors=return_tensors,
-    #         **kwargs,
-    #     )
 __all__ = ["NandiTokenizer"]

+    def encode(
+        self,
+        text,
+        text_pair=None,
+        add_special_tokens: bool = True,
+        padding=False,
+        truncation=None,
+        max_length=None,
+        stride: int = 0,
+        padding_side=None,
+        return_tensors=None,
+        **kwargs,
+    ):
+        if isinstance(text, str):
+            # This is a temporary fix to match the behaviour of the training pipeline
+            text = "<|im_start|>" + " " + text
+        return super().encode(
+            text,
+            text_pair=text_pair,
+            add_special_tokens=add_special_tokens,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            stride=stride,
+            padding_side=padding_side,
+            return_tensors=return_tensors,
+            **kwargs,
+        )
 __all__ = ["NandiTokenizer"]