Instructions to use ajibawa-2023/carl-llama-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/carl-llama-2-13b with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/carl-llama-2-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/carl-llama-2-13b")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/carl-llama-2-13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ajibawa-2023/carl-llama-2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/carl-llama-2-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/carl-llama-2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ajibawa-2023/carl-llama-2-13b

SGLang

How to use ajibawa-2023/carl-llama-2-13b 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 "ajibawa-2023/carl-llama-2-13b" \
    --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": "ajibawa-2023/carl-llama-2-13b",
		"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 "ajibawa-2023/carl-llama-2-13b" \
        --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": "ajibawa-2023/carl-llama-2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ajibawa-2023/carl-llama-2-13b with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/carl-llama-2-13b
```

carl-llama-2-13b / pytorch_model.bin-aa

ajibawa-2023

baae46d150b38705c2ca3c2a667edc460586ec84b7ea77d9854e556b3f01254d

5365fdb about 3 years ago

Download

History Blame

9.66 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: e1e5a04746e6e76f5eca7e9c284cf685364ac10c68089bcce7d684d928f25071
Size of remote file:: 9.66 GB
SHA256:: 83d639f18d001e120ce96ff76d1a3b0f821b2a8b827bcd3a5d5cfe112ffeed72

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.