Instructions to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with Transformers:

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

pipe = pipeline("text-generation", model="Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience

SGLang

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience 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 "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience" \
    --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": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"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 "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience" \
        --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": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with Docker Model Runner:
```
docker model run hf.co/Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience
```

Clemylia commited on Feb 6

Commit

8d57753

verified ·

1 Parent(s): fe8330f

Rename nelya_config (1).json to nelya_config.json

Browse files

Files changed (1) hide show

nelya_config (1).json → nelya_config.json +2 -2

nelya_config (1).json → nelya_config.json RENAMED Viewed

@@ -1,7 +1,7 @@
 {
-    "vocab_size": 7934,
     "hidden_size": 4096,
-    "num_layers": 12,
     "num_heads": 32,
     "intermediate_size": 8192,
     "max_pos": 128

 {
+    "vocab_size": 11705,
     "hidden_size": 4096,
+    "num_layers": 32,
     "num_heads": 32,
     "intermediate_size": 8192,
     "max_pos": 128