Instructions to use Ilya626/Cydonia_Vistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ilya626/Cydonia_Vistral with Transformers:

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

pipe = pipeline("text-generation", model="Ilya626/Cydonia_Vistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ilya626/Cydonia_Vistral")
model = AutoModelForCausalLM.from_pretrained("Ilya626/Cydonia_Vistral", 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 Ilya626/Cydonia_Vistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ilya626/Cydonia_Vistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ilya626/Cydonia_Vistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ilya626/Cydonia_Vistral

SGLang

How to use Ilya626/Cydonia_Vistral 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 "Ilya626/Cydonia_Vistral" \
    --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": "Ilya626/Cydonia_Vistral",
		"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 "Ilya626/Cydonia_Vistral" \
        --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": "Ilya626/Cydonia_Vistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ilya626/Cydonia_Vistral with Docker Model Runner:
```
docker model run hf.co/Ilya626/Cydonia_Vistral
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

/workspace/Vistral-24B-Instruct
/workspace/Cydonia-24B-v4.2.0

Configuration

The following YAML configuration was used to produce this model:

# --- Gradient SLERP: Vistral (RU) x Cydonia (RP) ---
merge_method: slerp
base_model: /workspace/Vistral-24B-Instruct   # SLERP anchor (0.0 = pure Vistral)
dtype: bfloat16

models:
  - model: /workspace/Vistral-24B-Instruct
  - model: /workspace/Cydonia-24B-v4.2.0

parameters:
  # Smooth profile "more Vistral (RU) at the bottom, more Cydonia (RP) at the top"
  # 5 nodes will be uniformly interpolated across all transformer layers.
  t:
    - filter: self_attn
      value: [0.35, 0.45, 0.55, 0.60, 0.65]  # lower — closer to Vistral, higher — to Cydonia
    - filter: mlp
      value: [0.30, 0.45, 0.55, 0.60, 0.60]
    - value: 0.50  # default for other tensors (if any were not covered by the filters)

  # SLERP also for embed/lm_head with different vocabularies (only available for 2 models)
  embed_slerp: true

# Building a combined vocabulary to correctly resolve the "+2 
tokenizer_source: union

Chat templates: Llama 3 OR Mistral Tekken

Downloads last month: 14

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for Ilya626/Cydonia_Vistral

TheDrummer/Cydonia-24B-v4.2.0

Vikhrmodels/Vistral-24B-Instruct

Merge model

this model

Quantizations

2 models