Instructions to use jan-ai/Pandora-13B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jan-ai/Pandora-13B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="jan-ai/Pandora-13B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jan-ai/Pandora-13B-v1")
model = AutoModelForCausalLM.from_pretrained("jan-ai/Pandora-13B-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jan-ai/Pandora-13B-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jan-ai/Pandora-13B-v1

SGLang

How to use jan-ai/Pandora-13B-v1 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 "jan-ai/Pandora-13B-v1" \
    --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": "jan-ai/Pandora-13B-v1",
		"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 "jan-ai/Pandora-13B-v1" \
        --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": "jan-ai/Pandora-13B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jan-ai/Pandora-13B-v1 with Docker Model Runner:
```
docker model run hf.co/jan-ai/Pandora-13B-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WARNING

This is a model file only for evaluation. Please use the model here:

Model: Pandora-v1-13B
GGUF: Pandora-v1-13B-GGUF

Jan - Discord

Model Description

This model uses the passthrough merge method from the best 7B models on the OpenLLM Leaderboard:

The yaml config file for this model is here:

slices:
  - sources:
    - model: "viethq188/LeoScorpius-7B-Chat-DPO"
      layer_range: [0, 24]
  - sources:
    - model: "GreenNode/GreenNodeLM-7B-v1olet"
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Prompt template

ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Run this model

You can run this model using Jan on Mac, Windows, or Linux.

Jan is an open source, ChatGPT alternative that is:

💻 100% offline on your machine: Your conversations remain confidential, and visible only to you.

🗂️ An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.

🌐 OpenAI Compatible: Local server on port 1337 with OpenAI compatible endpoints

🌍 Open Source & Free: We build in public; check out our Github

Please use the Pandora-v1-13B-GGUF when using on Jan.

About Jan

Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.

Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.

Jan Model Merger

This is a test project for merging models.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here.

Metric	Value
Avg.	?
ARC (25-shot)	?
HellaSwag (10-shot)	?
MMLU (5-shot)	?
TruthfulQA (0-shot)	?
Winogrande (5-shot)	?
GSM8K (5-shot)	?

Acknowlegement

Downloads last month: 210

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for jan-ai/Pandora-13B-v1

Quantizations

1 model