Instructions to use Heejindo/rationale_model_e10_save5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heejindo/rationale_model_e10_save5000 with Transformers:

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

pipe = pipeline("text-generation", model="Heejindo/rationale_model_e10_save5000")

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

tokenizer = AutoTokenizer.from_pretrained("Heejindo/rationale_model_e10_save5000")
model = AutoModelForCausalLM.from_pretrained("Heejindo/rationale_model_e10_save5000", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Heejindo/rationale_model_e10_save5000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Heejindo/rationale_model_e10_save5000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heejindo/rationale_model_e10_save5000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Heejindo/rationale_model_e10_save5000

SGLang

How to use Heejindo/rationale_model_e10_save5000 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 "Heejindo/rationale_model_e10_save5000" \
    --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": "Heejindo/rationale_model_e10_save5000",
		"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 "Heejindo/rationale_model_e10_save5000" \
        --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": "Heejindo/rationale_model_e10_save5000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Heejindo/rationale_model_e10_save5000 with Docker Model Runner:
```
docker model run hf.co/Heejindo/rationale_model_e10_save5000
```

Heejindo commited on Dec 3, 2024

Commit

fae471b

verified ·

1 Parent(s): a50bc27

Training in progress, step 35000

Browse files

Files changed (1) hide show

model.safetensors +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c1591265a4abec3475614bb33480f981d8746e93f4b22ea960e73a89d0ebb7a5
 size 4943274328

 version https://git-lfs.github.com/spec/v1
+oid sha256:3fae868ef8bc0b2709e3eca1a7a857b3c033774c5332b25a57b16d7666000866
 size 4943274328