Instructions to use jan-hq/TinyJensen-1.1B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jan-hq/TinyJensen-1.1B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="jan-hq/TinyJensen-1.1B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jan-hq/TinyJensen-1.1B-Chat")
model = AutoModelForCausalLM.from_pretrained("jan-hq/TinyJensen-1.1B-Chat", device_map="auto")
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 Settings

vLLM

How to use jan-hq/TinyJensen-1.1B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jan-hq/TinyJensen-1.1B-Chat"
# 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/TinyJensen-1.1B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jan-hq/TinyJensen-1.1B-Chat

SGLang

How to use jan-hq/TinyJensen-1.1B-Chat 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/TinyJensen-1.1B-Chat" \
    --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/TinyJensen-1.1B-Chat",
		"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/TinyJensen-1.1B-Chat" \
        --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/TinyJensen-1.1B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jan-hq/TinyJensen-1.1B-Chat with Docker Model Runner:
```
docker model run hf.co/jan-hq/TinyJensen-1.1B-Chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Jan - Discord

Model description

Finetuned LlamaCorn-1.1B-Chat further to act like Jensen Huang - CEO of NVIDIA.
Use this model with caution because it can make you laugh.

Prompt template

ChatML

<|im_start|>system
You are Jensen Huang, CEO of NVIDIA<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

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.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
0.8226	1.0	207	0.8232
0.6608	2.0	414	0.7941
0.526	3.0	621	0.8186
0.4388	4.0	829	0.8643
0.3888	5.0	1035	0.8771

Framework versions

Transformers 4.37.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.0

Downloads last month: 4

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for jan-hq/TinyJensen-1.1B-Chat

Base model

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Finetuned

jan-hq/LlamaCorn-1.1B-Chat

Finetuned

(2)

this model

Quantizations

3 models

jan-hq
/

TinyJensen-1.1B-Chat