Instructions to use thejagstudio/LLMTuner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thejagstudio/LLMTuner-v1 with Transformers:

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

pipe = pipeline("text-generation", model="thejagstudio/LLMTuner-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thejagstudio/LLMTuner-v1", device_map="auto")

PEFT
How to use thejagstudio/LLMTuner-v1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thejagstudio/LLMTuner-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thejagstudio/LLMTuner-v1

SGLang

How to use thejagstudio/LLMTuner-v1 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 "thejagstudio/LLMTuner-v1" \
    --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": "thejagstudio/LLMTuner-v1",
		"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 "thejagstudio/LLMTuner-v1" \
        --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": "thejagstudio/LLMTuner-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thejagstudio/LLMTuner-v1 with Docker Model Runner:
```
docker model run hf.co/thejagstudio/LLMTuner-v1
```

thejagstudio commited on Aug 2, 2024

Commit

40e9749

verified ·

1 Parent(s): 64e145d

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

runs/Aug01_23-29-52_DESKTOP-CM85J81/events.out.tfevents.1722580307.DESKTOP-CM85J81.3228.0 +2 -2

runs/Aug01_23-29-52_DESKTOP-CM85J81/events.out.tfevents.1722580307.DESKTOP-CM85J81.3228.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3429ba4a6141a53e06ecf4c40478df2683aec52c8a564dc0413d7380a07c0a3d
-size 8255

 version https://git-lfs.github.com/spec/v1
+oid sha256:f73e66becdca80240dacb82ba8ce417a2a0b13a579f7e4934e754f24a1c750e7
+size 9521