Instructions to use jan-hq/trinity-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jan-hq/trinity-v1 with Transformers:

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

pipe = pipeline("text-generation", model="jan-hq/trinity-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jan-hq/trinity-v1")
model = AutoModelForCausalLM.from_pretrained("jan-hq/trinity-v1")
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 jan-hq/trinity-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jan-hq/trinity-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jan-hq/trinity-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jan-hq/trinity-v1

SGLang

How to use jan-hq/trinity-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-hq/trinity-v1" \
    --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": "jan-hq/trinity-v1",
		"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 "jan-hq/trinity-v1" \
        --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": "jan-hq/trinity-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Jan - Discord

Model Description

This model uses the Slerp merge method from the best models on 14th Dec on the OpenLLM Leaderboard:

base model: GreenNode/GreenNodeLM-7B-v1olet

The yaml config file for this model is here:

slices:
  - sources:
      - model: viethq188/LeoScorpius-7B-Chat-DPO
        layer_range: [0, 32] 
      - model: GreenNode/GreenNodeLM-7B-v1olet
        layer_range: [0, 32]
merge_method: slerp
base_model: GreenNode/GreenNodeLM-7B-v1olet
parameters:
  t:
    - filter: lm_head 
      value: [0.55]
    - filter: embed_tokens
      value: [0.7]
    - filter: self_attn
      value: [0.65, 0.35]
    - filter: mlp
      value:  [0.35, 0.65]
    - filter: layernorm
      value: [0.4, 0.6]
    - filter: modelnorm
      value: [0.6]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

Thank you Undi95 for the secret sauce and Charles Goddard for mergekit.

Prompt template

Work best on:

{system_message}
### Instruction:
{prompt}

### Response:

Run this model

You can run this model using Jan Desktop 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

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.