Not-For-All-Audiences

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text-generation-inference

3-bit

exl2

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Instructions to use kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2")
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 kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2

SGLang

How to use kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 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 "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2" \
    --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": "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2",
		"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 "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2" \
        --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": "kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2
```

L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2 / README.md

kim512

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0177b23 verified about 2 years ago

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	---
	tags:
	- not-for-all-audiences
	---

	# EXL2 quants of [crestf411/L3-70B-daybreak-storywriter-v0.4](https://huggingface.co/crestf411/L3-70B-daybreak-storywriter-v0.4)

	[3.00 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-3.0bpw-h6-exl2)
	[3.50 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-3.5bpw-h6-exl2)
	[4.00 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-4.0bpw-h6-exl2)
	[4.50 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-4.5bpw-h6-exl2)
	[5.00 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-5.0bpw-h6-exl2)
	[6.00 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-6.0bpw-h6-exl2)
	[8.00 bits per weight](https://huggingface.co/kim512/L3-70B-daybreak-storywriter-v0.4-8.0bpw-h8-exl2)

	Created using the defaults from exllamav2 0.0.21 convert.py
	3.0bpw to 6.0bpw head bits = 6
	8.0bpw head bits = 8
	length = 8192
	dataset rows = 200
	measurement rows = 32
	measurement length = 8192

	# L3-70B-daybreak-storywriter-v0.4

	Daybreak (2024 May 24) v0.4 LoRA on top of https://huggingface.co/tdrussell/Llama-3-70B-Instruct-Storywriter

	Dataset curation to remove slop-perceived expressions continues.

	The below regexes return 0 matches. Bold entries are new since v0.3.

	* 'barely above a whisper',
	* 'barely audible',
	* 'shiver([s]?) down',
	* ' ministration',
	* 'audible (["\'"]?)p[l]?op',
	* 'can\'t help but',
	* 'buck([s]?) my ',
	* 'buck([s]?) h[ei][rs] ',
	* '[Dd]espite h[ie][mr]self',
	* 'slick slit',
	* 'whatever it takes',
	* 'unlike anything (s?)he',
	* *'a mix([a-z]) of',**
	* 'wave after wave',
	* 'reckless abandon',
	* '[Mm]aybe, just maybe',
	* 'eyes gleaming',
	* 'mischievously',
	* "couldn't help but",

	From testing so far, it feels like temperature 0.8-0.9 is a good starting point. I have mostly tested with everything neutralized. Please give feedback on which parameters work good for you.