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
```

Nandi-Mini-600M-Early-Checkpoint

File size: 1,011 Bytes

{
  "architectures": [
    "NandiForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_nandi.NandiConfig",
    "AutoModel": "modeling_nandi.NandiModel",
    "AutoModelForCausalLM": "modeling_nandi.NandiForCausalLM"
  },
  "model_type": "nandi",
  "vocab_size": 131072,
  "hidden_size": 1248,
  "intermediate_size": 3556,
  "num_hidden_layers": 28,
  "num_attention_heads": 16,
  "num_key_value_heads": 8,
  "head_dim": 78,
  "hidden_act": "silu",
  "max_position_embeddings": 2048,
  "initializer_range": 0.02,
  "rms_norm_eps": 1e-06,
  "use_cache": true,
  "pad_token_id": null,
  "bos_token_id": 1,
  "eos_token_id": 0,
  "tie_word_embeddings": true,
  "rope_parameters": {
    "rope_theta": 1000000.0
  },
  "attention_bias": false,
  "attention_dropout": 0.0,
  "mlp_bias": false,
  "factorized_embedding": false,
  "embedding_rank": 768,
  "layer_sharing": false,
  "layer_sharing_repeats": 2,
  "qk_norm": true,
  "shared_kv": true,
  "kv_cache_mode": "shared",
  "torch_dtype": "bfloat16"
}