Instructions to use KitsuVp/NeoLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KitsuVp/NeoLLM with Transformers:

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

pipe = pipeline("text-generation", model="KitsuVp/NeoLLM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KitsuVp/NeoLLM", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KitsuVp/NeoLLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KitsuVp/NeoLLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KitsuVp/NeoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KitsuVp/NeoLLM

SGLang

How to use KitsuVp/NeoLLM 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 "KitsuVp/NeoLLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KitsuVp/NeoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "KitsuVp/NeoLLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KitsuVp/NeoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KitsuVp/NeoLLM with Docker Model Runner:
```
docker model run hf.co/KitsuVp/NeoLLM
```

KitsuVp commited on Jan 26

Commit

fcbfce7

verified ·

1 Parent(s): fed91a6

Update modeling_neollm.py

Browse files

Files changed (1) hide show

modeling_neollm.py +2 -10

modeling_neollm.py CHANGED Viewed

@@ -1210,16 +1210,8 @@ class NeoLLMForCausalLM(NeoLLMPreTrainedModel, GenerationMixin):
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.post_init()
-    def tie_weights(self, missing_keys=None, recompute_mapping=True):
-        """
-        Tie the weights between the input embeddings and the output embeddings.
-        Required for v5.0 compatibility.
-        Args:
-            missing_keys: List of missing keys from checkpoint loading (v5.0 parameter)
-            recompute_mapping: Whether to recompute tied weights mapping (v5.0 parameter)
-        """
-        self._tie_or_clone_weights(self.lm_head, self.model.embed_tokens)
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,

         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.post_init()
+        _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,