Instructions to use MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2", 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 MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-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": "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2

SGLang

How to use MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-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 "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-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": "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-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 "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-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": "MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/MetaphoricalCode/Eurydice-24b-v3-8.0bpw-h8-exl2
```

Eurydice-24b-v3-8.0bpw-h8-exl2

File size: 2,382 Bytes

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---
base_model: aixonlab/Eurydice-24b-v3
base_model_relation: quantized
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Quantization
Quantized using the default exllamav2 (0.2.9) quantization process.\
Original model: https://huggingface.co/aixonlab/Eurydice-24b-v3 \
exllamav2: https://github.com/turboderp-org/exllamav2

![Eurydice 24b Banner](https://cdn-uploads.huggingface.co/production/uploads/66dcee3321f901b049f48002/J-uJLlBR_i0HTORt_01WF.png)

# Eurydice 24b v3 🧙‍♂️


Eurydice 24b v3 is designed to be the perfect companion for multi-role conversations. It demonstrates exceptional contextual understanding and excels in creativity, natural conversation and storytelling. Built on Mistral 3.1, this model has been trained on a custom dataset specifically crafted to enhance its capabilities.

## Model Details 📊

- **Developed by:** Aixon Lab
- **Model type:** Causal Language Model
- **Language(s):** English (primarily), may support other languages
- **License:** Apache 2.0
- **Repository:** https://huggingface.co/aixonlab/Eurydice-24b-v3

## Quantization
- **GGUF:** https://huggingface.co/mradermacher/Eurydice-24b-v3-GGUF

## Model Architecture 🏗️

- **Base model:** aixonlab/Eurydice-24b-v2
- **Parameter count:** ~24 billion
- **Architecture specifics:** Transformer-based language model

## Intended Use 🎯
As an advanced language model for various natural language processing tasks, including but not limited to text generation (excels in chat), question-answering, and analysis.

## Ethical Considerations 🤔
As a model based on multiple sources, Eurydice 24b v3 may inherit biases and limitations from its constituent models. Users should be aware of potential biases in generated content and use the model responsibly.

## Performance and Evaluation
Performance metrics and evaluation results for Eurydice 24b v3 are yet to be determined. Users are encouraged to contribute their findings and benchmarks.

## Limitations and Biases
The model may exhibit biases present in its training data and constituent models. It's crucial to critically evaluate the model's outputs and use them in conjunction with human judgment.

## Additional Information
For more details on the base model and constituent models, please refer to their respective model cards and documentation.