Instructions to use FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw with Transformers:

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

pipe = pipeline("text-generation", model="FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw")
model = AutoModelForCausalLM.from_pretrained("FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw", 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 FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw

SGLang

How to use FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw 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 "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw" \
    --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": "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw",
		"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 "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw" \
        --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": "FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw with Docker Model Runner:
```
docker model run hf.co/FluffyKaeloky/L3.3-70B-Euryale-v2.3-exl2-3.0bpw
```

L3.3-70B-Euryale-v2.3-exl2-3.0bpw / README.md

FluffyKaeloky

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	---
	library_name: transformers
	license: llama3
	base_model: Sao10K/L3.3-70B-Euryale-v2.3
	base_model_relation: quantized
	quantized_by: FluffyKaeloky
	tags:
	- generated_from_trainer
	model-index:
	- name: L3.3-70B-Euryale-v2.3
	results: []
	---

	![eury](https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3/resolve/main/Eury.png)

	# L3.3-70B-Euryale-v2.3

	A direct replacement / successor to Euryale v2.2, not Hanami-x1, though it is slightly better than them in my opinion.

	This is entirely trained on top of Llama 3.3 Instruct, not Lora-extracted which is all the rage.

	Recommended Model Settings \| Look, I just use these, they work fine enough. I don't even know how DRY or other meme samplers work. Your system prompt matters more anyway.
	```
	Prompt Format: Llama-3-Instruct
	Temperature: 1.1
	min_p: 0.1
	```


	Future-ish plans:
	<br>\- Further refine the Datasets used for quality, more secondary chats, more creative-related domains.
	<br>\- Work on my other incomplete projects. About half a dozen on the backburner for a while now.

	Special thanks to my wallet for funding this, my juniors who share a single braincell between them, and my current national service.

	Have a good day, don't shit yourselves friends. I had a nasty call today.

	Also sorry for the inactivity. Life was in the way. It still is, just less so, for now. Burnout is a thing, huh?

	https://sao10k.carrd.co/ for contact.

	---

	[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
	<details><summary>See axolotl config</summary>

	axolotl version: `0.5.2`
	```yaml
	base_model: meta-llama/Llama-3.3-70B-Instruct
	model_type: AutoModelForCausalLM
	tokenizer_type: AutoTokenizer

	load_in_8bit: false
	load_in_4bit: false
	strict: false
	sequence_len: 16384
	bf16: auto
	fp16:
	tf32: false
	flash_attention: true

	adapter: lora
	lora_model_dir:
	lora_r: 128
	lora_alpha: 16
	lora_dropout: 0.1
	lora_target_linear: true
	lora_fan_in_fan_out:
	peft_use_rslora: true

	# Data
	dataset_prepared_path: last_run_prepared
	datasets:
	- path: datasets/amoral-full-sys-prompt.json # Unalignment Data - Cleaned Up from Original, Split to its own file
	type: customllama3
	- path: datasets/mimi-superfix-RP-filtered-fixed.json # RP / Creative-Instruct Data
	type: customllama3
	- path: datasets/hespera-smartshuffle.json # Hesperus-v2-Instruct Data
	type: customllama3
	warmup_steps: 15

	plugins:
	- axolotl.integrations.liger.LigerPlugin
	liger_rope: true
	liger_rms_norm: true
	liger_layer_norm: true
	liger_glu_activation: true
	liger_fused_linear_cross_entropy: true

	# Iterations
	num_epochs: 1

	# Batching
	gradient_accumulation_steps: 4
	micro_batch_size: 1
	gradient_checkpointing: "unsloth"

	# Optimizer
	optimizer: paged_ademamix_8bit
	lr_scheduler: cosine
	learning_rate: 0.000004
	weight_decay: 0.1
	max_grad_norm: 25.0

	# Iterations
	num_epochs: 1

	# Misc
	deepspeed: ./deepspeed_configs/zero3_bf16.json
	```

	</details><br>

	---

	```
	Art by てぃあ
	https://www.pixiv.net/en/users/724263
	```