Instructions to use DedeProGames/mini-chennus-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/mini-chennus-2 with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/mini-chennus-2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/mini-chennus-2")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/mini-chennus-2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/mini-chennus-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/mini-chennus-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/mini-chennus-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DedeProGames/mini-chennus-2

SGLang

How to use DedeProGames/mini-chennus-2 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 "DedeProGames/mini-chennus-2" \
    --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": "DedeProGames/mini-chennus-2",
		"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 "DedeProGames/mini-chennus-2" \
        --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": "DedeProGames/mini-chennus-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/mini-chennus-2 with Docker Model Runner:
```
docker model run hf.co/DedeProGames/mini-chennus-2
```

DedeProGames commited on Feb 21

Commit

7df8fc5

verified ·

1 Parent(s): ec9df04

Training in progress, step 4600

Browse files

Files changed (1) hide show

runs/Feb21_10-40-52_c83a631fdb95/events.out.tfevents.1771670452.c83a631fdb95.1165.0 +2 -2

runs/Feb21_10-40-52_c83a631fdb95/events.out.tfevents.1771670452.c83a631fdb95.1165.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:52e5e469e453d3f7544247f879df0a3a2294307e94dbe3dfb99eede3b5f82864
-size 30099

 version https://git-lfs.github.com/spec/v1
+oid sha256:0d8b4fdcbe6959ea1c3871a2fe9d19e79e8d559737117add4ef1bd0f1e413b5b
+size 31266