Instructions to use JDWebProgrammer/Mistral-CultriX-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JDWebProgrammer/Mistral-CultriX-slerp with Transformers:

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

pipe = pipeline("text-generation", model="JDWebProgrammer/Mistral-CultriX-slerp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JDWebProgrammer/Mistral-CultriX-slerp")
model = AutoModelForCausalLM.from_pretrained("JDWebProgrammer/Mistral-CultriX-slerp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JDWebProgrammer/Mistral-CultriX-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JDWebProgrammer/Mistral-CultriX-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JDWebProgrammer/Mistral-CultriX-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JDWebProgrammer/Mistral-CultriX-slerp

SGLang

How to use JDWebProgrammer/Mistral-CultriX-slerp 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 "JDWebProgrammer/Mistral-CultriX-slerp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JDWebProgrammer/Mistral-CultriX-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "JDWebProgrammer/Mistral-CultriX-slerp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JDWebProgrammer/Mistral-CultriX-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Mistral-CultriX-slerp

Research & Development for AutoSynthetix AI

🌐 Website https://autosynthetix.com/

📨 Discord https://discord.gg/pAKqENStQr

📦 GitHub https://github.com/jdwebprogrammer

📦 GitLab https://gitlab.com/jdwebprogrammer

🏆 Patreon https://patreon.com/jdwebprogrammer

📷 YouTube https://www.youtube.com/@jdwebprogrammer

📺 Twitch https://www.twitch.tv/jdwebprogrammer

🐦 Twitter(X) https://twitter.com/jdwebprogrammer

License includes the license of the model derivatives:

MergeKit LGPL-3.0 https://github.com/arcee-ai/mergekit?tab=LGPL-3.0-1-ov-file#readme
Mistral Apache 2.0 https://huggingface.co/mistralai/Mistral-7B-v0.1
CultriX Apache 2.0 https://huggingface.co/CultriX/NeuralTrix-7B-dpo

Mistral-CultriX-slerp is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 32]
      - model: CultriX/NeuralTrix-7B-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "JDWebProgrammer/Mistral-CultriX-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for JDWebProgrammer/Mistral-CultriX-slerp

CultriX/NeuralTrix-7B-dpo

mistralai/Mistral-7B-v0.1

Merge model

this model

Quantizations

1 model