Instructions to use Sao10K/L3.3-70B-Euryale-v2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sao10K/L3.3-70B-Euryale-v2.3 with Transformers:

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

pipe = pipeline("text-generation", model="Sao10K/L3.3-70B-Euryale-v2.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sao10K/L3.3-70B-Euryale-v2.3")
model = AutoModelForCausalLM.from_pretrained("Sao10K/L3.3-70B-Euryale-v2.3", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sao10K/L3.3-70B-Euryale-v2.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sao10K/L3.3-70B-Euryale-v2.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sao10K/L3.3-70B-Euryale-v2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sao10K/L3.3-70B-Euryale-v2.3

SGLang

How to use Sao10K/L3.3-70B-Euryale-v2.3 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 "Sao10K/L3.3-70B-Euryale-v2.3" \
    --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": "Sao10K/L3.3-70B-Euryale-v2.3",
		"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 "Sao10K/L3.3-70B-Euryale-v2.3" \
        --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": "Sao10K/L3.3-70B-Euryale-v2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sao10K/L3.3-70B-Euryale-v2.3 with Docker Model Runner:
```
docker model run hf.co/Sao10K/L3.3-70B-Euryale-v2.3
```

Sao10K commited on Dec 8, 2024

Commit

7656ef9

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Create README.md

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+---
+library_name: transformers
+license: llama3
+base_model: meta-llama/Llama-3.3-70B-Instruct
+tags:
+- generated_from_trainer
+model-index:
+- name: L3.3-70B-Euryale-v2.3
+  results: []
+---
+)
+# L3.3-70B-Euryale-v2.3
+A direct replacement / successor to Euryale v2.2, not Hanami-x1, though it is slightly better in my opinion.
+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>\- Complete this model series.
+<br>\- Further refine the Datasets used for quality, more secondary chats, more creative-related domains. (Inspired by Drummer)
+<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.
+<br>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>