Instructions to use impossiblecommute/justin_297 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use impossiblecommute/justin_297 with Transformers:

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

pipe = pipeline("text-generation", model="impossiblecommute/justin_297")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("impossiblecommute/justin_297")
model = AutoModelForCausalLM.from_pretrained("impossiblecommute/justin_297")
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_297 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "impossiblecommute/justin_297"
# 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_297",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/impossiblecommute/justin_297

SGLang

How to use impossiblecommute/justin_297 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_297" \
    --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_297",
		"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_297" \
        --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_297",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use impossiblecommute/justin_297 with Docker Model Runner:
```
docker model run hf.co/impossiblecommute/justin_297
```

justin_297

Commit History

Upload tokenizer

35f3be6
verified

David Cole commited on Sep 27, 2024

Upload LlamaForCausalLM

91912b2
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David Cole commited on Sep 27, 2024

initial commit

d397bf0
verified

David Cole commited on Sep 27, 2024

Commit History

Upload tokenizer 35f3be6 verified

Upload LlamaForCausalLM 91912b2 verified

initial commit d397bf0 verified

Upload tokenizer

35f3be6
verified

Upload LlamaForCausalLM

91912b2
verified

initial commit

d397bf0
verified