Instructions to use dusersad12/MyNewModel-ReleaseRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dusersad12/MyNewModel-ReleaseRepo with Transformers:

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

pipe = pipeline("text-generation", model="dusersad12/MyNewModel-ReleaseRepo")

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

tokenizer = AutoTokenizer.from_pretrained("dusersad12/MyNewModel-ReleaseRepo")
model = AutoModelForCausalLM.from_pretrained("dusersad12/MyNewModel-ReleaseRepo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dusersad12/MyNewModel-ReleaseRepo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dusersad12/MyNewModel-ReleaseRepo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dusersad12/MyNewModel-ReleaseRepo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dusersad12/MyNewModel-ReleaseRepo

SGLang

How to use dusersad12/MyNewModel-ReleaseRepo 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 "dusersad12/MyNewModel-ReleaseRepo" \
    --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": "dusersad12/MyNewModel-ReleaseRepo",
		"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 "dusersad12/MyNewModel-ReleaseRepo" \
        --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": "dusersad12/MyNewModel-ReleaseRepo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dusersad12/MyNewModel-ReleaseRepo with Docker Model Runner:
```
docker model run hf.co/dusersad12/MyNewModel-ReleaseRepo
```

dusersad12 commited on 24 days ago

Commit

1b40704

verified ·

1 Parent(s): 4a53552

Add best checkpoint (step_2000) weights and config

Browse files

Files changed (2) hide show

config.json +4 -0
pytorch_model.bin +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,4 @@

+{
+    "model_type": "llama",
+    "architectures": ["LlamaForCausalLM"]
+  }

pytorch_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a58a601fa23dabab958693a2fc2d77c012f21093d943a91aa09c69f2d990254f
+size 30