Instructions to use impossiblecommute/justin_296 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use impossiblecommute/justin_296 with Transformers:

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

pipe = pipeline("text-generation", model="impossiblecommute/justin_296")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("impossiblecommute/justin_296")
model = AutoModelForCausalLM.from_pretrained("impossiblecommute/justin_296")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use impossiblecommute/justin_296 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "impossiblecommute/justin_296"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impossiblecommute/justin_296",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/impossiblecommute/justin_296

SGLang

How to use impossiblecommute/justin_296 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 "impossiblecommute/justin_296" \
    --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": "impossiblecommute/justin_296",
		"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 "impossiblecommute/justin_296" \
        --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": "impossiblecommute/justin_296",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use impossiblecommute/justin_296 with Docker Model Runner:
```
docker model run hf.co/impossiblecommute/justin_296
```

justin_296

Commit History

Upload LlamaForCausalLM

5d46c96
verified

David Cole commited on Sep 27, 2024

Upload tokenizer

3f968cf
verified

David Cole commited on Sep 27, 2024

initial commit

17c41f2
verified

David Cole commited on Sep 27, 2024

Commit History

Upload LlamaForCausalLM 5d46c96 verified

Upload tokenizer 3f968cf verified

initial commit 17c41f2 verified

Upload LlamaForCausalLM

5d46c96
verified

Upload tokenizer

3f968cf
verified

initial commit

17c41f2
verified