Instructions to use andreaskoepf/llama2-13b-oasst-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreaskoepf/llama2-13b-oasst-baseline with Transformers:

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

pipe = pipeline("text-generation", model="andreaskoepf/llama2-13b-oasst-baseline")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreaskoepf/llama2-13b-oasst-baseline")
model = AutoModelForCausalLM.from_pretrained("andreaskoepf/llama2-13b-oasst-baseline")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use andreaskoepf/llama2-13b-oasst-baseline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreaskoepf/llama2-13b-oasst-baseline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreaskoepf/llama2-13b-oasst-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andreaskoepf/llama2-13b-oasst-baseline

SGLang

How to use andreaskoepf/llama2-13b-oasst-baseline 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 "andreaskoepf/llama2-13b-oasst-baseline" \
    --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": "andreaskoepf/llama2-13b-oasst-baseline",
		"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 "andreaskoepf/llama2-13b-oasst-baseline" \
        --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": "andreaskoepf/llama2-13b-oasst-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use andreaskoepf/llama2-13b-oasst-baseline with Docker Model Runner:
```
docker model run hf.co/andreaskoepf/llama2-13b-oasst-baseline
```

wandb: public-sft/runs/run32_oasst1_13b
sampling report

Prompt template

chatml format is used: "<|im_start|>system\n{system message}<|im_end|>\n<|im_start|>user\n{user prompt}<|im_end|>\n<|im_start|>assistant\n{Assistant answer}<|im_end|>\n"

multi-line:

"""
<|im_start|>system
{system message}<|im_end|>
<|im_start|>user
{user prompt}<|im_end|>
<|im_start|>assistant
{Assistant answer}<|im_end|>
"""

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llama2-13b-oasst-baseline

Prompt template

Dataset used to train andreaskoepf/llama2-13b-oasst-baseline