Instructions to use obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2 with Transformers:

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

pipe = pipeline("text-generation", model="obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2")
model = AutoModelForCausalLM.from_pretrained("obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2

SGLang

How to use obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2 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 "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2" \
    --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": "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2",
		"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 "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2" \
        --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": "obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2 with Docker Model Runner:
```
docker model run hf.co/obrmmk/tinycodellama-jp-0.6b-20k-2-instruct-jcodealpaca-py-def2
```

obrmmk commited on Jan 16, 2024

Commit

cbcba67

verified ·

1 Parent(s): 259d26e

Upload generation_config.json with huggingface_hub

Browse files

Files changed (1) hide show

generation_config.json +7 -0

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 7,
+  "eos_token_id": 7,
+  "pad_token_id": 4,
+  "transformers_version": "4.36.2"
+}