Instructions to use totally-not-an-llm/PuddleJumper-13b-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use totally-not-an-llm/PuddleJumper-13b-V2 with Transformers:

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

pipe = pipeline("text-generation", model="totally-not-an-llm/PuddleJumper-13b-V2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("totally-not-an-llm/PuddleJumper-13b-V2")
model = AutoModelForCausalLM.from_pretrained("totally-not-an-llm/PuddleJumper-13b-V2")

Inference
Local Apps Settings

vLLM

How to use totally-not-an-llm/PuddleJumper-13b-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "totally-not-an-llm/PuddleJumper-13b-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "totally-not-an-llm/PuddleJumper-13b-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/totally-not-an-llm/PuddleJumper-13b-V2

SGLang

How to use totally-not-an-llm/PuddleJumper-13b-V2 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 "totally-not-an-llm/PuddleJumper-13b-V2" \
    --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": "totally-not-an-llm/PuddleJumper-13b-V2",
		"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 "totally-not-an-llm/PuddleJumper-13b-V2" \
        --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": "totally-not-an-llm/PuddleJumper-13b-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use totally-not-an-llm/PuddleJumper-13b-V2 with Docker Model Runner:
```
docker model run hf.co/totally-not-an-llm/PuddleJumper-13b-V2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merge of EverythingLM-V3-13b QLoRa and OpenOrca-Platypus2-13B.

Prompt format:

USER: <prompt>
ASSISTANT:

Quants:

https://huggingface.co/TheBloke/PuddleJumper-13B-V2-GGUF

https://huggingface.co/TheBloke/PuddleJumper-13B-V2-AWQ

https://huggingface.co/TheBloke/PuddleJumper-13B-V2-GPTQ

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	49.69
ARC (25-shot)	57.0
HellaSwag (10-shot)	81.06
MMLU (5-shot)	58.3
TruthfulQA (0-shot)	52.66
Winogrande (5-shot)	72.45
GSM8K (5-shot)	3.64
DROP (3-shot)	22.74

Downloads last month: 368

Model tree for totally-not-an-llm/PuddleJumper-13b-V2

Quantizations

4 models

totally-not-an-llm
/

PuddleJumper-13b-V2

Prompt format:

Quants:

Open LLM Leaderboard Evaluation Results

Model tree for totally-not-an-llm/PuddleJumper-13b-V2

Datasets used to train totally-not-an-llm/PuddleJumper-13b-V2