Instructions to use FrontiersMind/Lumma-0.6B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Lumma-0.6B-Base with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Lumma-0.6B-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Lumma-0.6B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrontiersMind/Lumma-0.6B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Lumma-0.6B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Lumma-0.6B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FrontiersMind/Lumma-0.6B-Base

SGLang

How to use FrontiersMind/Lumma-0.6B-Base 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/Lumma-0.6B-Base" \
    --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/Lumma-0.6B-Base",
		"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/Lumma-0.6B-Base" \
        --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/Lumma-0.6B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FrontiersMind/Lumma-0.6B-Base with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Lumma-0.6B-Base
```

vishesh-t27 commited on Jul 7

Commit

94a73ec

verified ·

1 Parent(s): 243a18d

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

config.json +42 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,42 @@

+{
+  "architectures": [
+    "NandiForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_nandi.NandiConfig",
+    "AutoModel": "modeling_nandi.NandiModel",
+    "AutoModelForCausalLM": "modeling_nandi.NandiForCausalLM"
+  },
+  "model_type": "nandi",
+  "vocab_size": 131072,
+  "hidden_size": 1440,
+  "intermediate_size": 3280,
+  "num_hidden_layers": 30,
+  "num_attention_heads": 16,
+  "num_key_value_heads": 8,
+  "head_dim": 90,
+  "hidden_act": "silu",
+  "max_position_embeddings": 12288,
+  "initializer_range": 0.006,
+  "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": true,
+  "embedding_rank": 512,
+  "layer_sharing": false,
+  "layer_sharing_repeats": 1,
+  "qk_norm": false,
+  "q_norm": true,
+  "shared_kv": true,
+  "kv_cache_mode": "shared",
+  "torch_dtype": "bfloat16"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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