Instructions to use elinas/chronos-13b-8k-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elinas/chronos-13b-8k-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="elinas/chronos-13b-8k-GPTQ", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("elinas/chronos-13b-8k-GPTQ", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("elinas/chronos-13b-8k-GPTQ", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use elinas/chronos-13b-8k-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "elinas/chronos-13b-8k-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "elinas/chronos-13b-8k-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/elinas/chronos-13b-8k-GPTQ

SGLang

How to use elinas/chronos-13b-8k-GPTQ 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 "elinas/chronos-13b-8k-GPTQ" \
    --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": "elinas/chronos-13b-8k-GPTQ",
		"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 "elinas/chronos-13b-8k-GPTQ" \
        --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": "elinas/chronos-13b-8k-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use elinas/chronos-13b-8k-GPTQ with Docker Model Runner:
```
docker model run hf.co/elinas/chronos-13b-8k-GPTQ
```

chronos-13b-8K-4bit

The original Chronos-13B model was merged with a LoRA trained on a majority of 1500 samples in the 8000 token range in the same style, with a cutoff of 8k tokens in full 8bit. It is meant to be used standalone, but if you would like the LoRA to merge/combine on your own, you can find it here https://huggingface.co/ZeusLabs/chronos-13b-8k-lora

The config.json includes modifications allowing extended context so you will need to use it with trust_remote_code if not using Exllama.

4bit (int4) quantized version using true-sequential and groupsize 128 of https://huggingface.co/elinas/chronos-13b plus https://huggingface.co/ZeusLabs/chronos-13b-8k-lora

This model is primarily focused on chat, roleplay, and storywriting, but can accomplish other tasks such as simple reasoning and coding.

Chronos generates very long outputs with coherent text, largely due to the human inputs it was trained on.

This model uses Alpaca formatting, so for optimal model performance, use:

### Instruction:
Your instruction or question here.
### Response:

Zeus Labs Discord

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