Instructions to use KitsuVp/NeoLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("KitsuVp/NeoLLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Update configuration_neollm.py
Browse files- configuration_neollm.py +3 -3
configuration_neollm.py
CHANGED
|
@@ -700,9 +700,9 @@ class NeoLLMConfig(PretrainedConfig):
|
|
| 700 |
use_seednorm=False,
|
| 701 |
use_lns=False,
|
| 702 |
use_gpas=False,
|
| 703 |
-
use_siamesenorm=
|
| 704 |
-
siamese_normalized_input=
|
| 705 |
-
siamese_depth_scaling=
|
| 706 |
siamese_attn_x_scale_init=1.0,
|
| 707 |
# ββ Embedding input normalization βββββββββββββββββββββββββββββββββ
|
| 708 |
use_embedding_input_norm=True,
|
|
|
|
| 700 |
use_seednorm=False,
|
| 701 |
use_lns=False,
|
| 702 |
use_gpas=False,
|
| 703 |
+
use_siamesenorm=True,
|
| 704 |
+
siamese_normalized_input=True,
|
| 705 |
+
siamese_depth_scaling=True,
|
| 706 |
siamese_attn_x_scale_init=1.0,
|
| 707 |
# ββ Embedding input normalization βββββββββββββββββββββββββββββββββ
|
| 708 |
use_embedding_input_norm=True,
|