Instructions to use kainatq/KaidenRp2400_12b_v1_m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kainatq/KaidenRp2400_12b_v1_m2 with Transformers:

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

pipe = pipeline("text-generation", model="kainatq/KaidenRp2400_12b_v1_m2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kainatq/KaidenRp2400_12b_v1_m2")
model = AutoModelForCausalLM.from_pretrained("kainatq/KaidenRp2400_12b_v1_m2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kainatq/KaidenRp2400_12b_v1_m2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kainatq/KaidenRp2400_12b_v1_m2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kainatq/KaidenRp2400_12b_v1_m2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kainatq/KaidenRp2400_12b_v1_m2

SGLang

How to use kainatq/KaidenRp2400_12b_v1_m2 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 "kainatq/KaidenRp2400_12b_v1_m2" \
    --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": "kainatq/KaidenRp2400_12b_v1_m2",
		"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 "kainatq/KaidenRp2400_12b_v1_m2" \
        --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": "kainatq/KaidenRp2400_12b_v1_m2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kainatq/KaidenRp2400_12b_v1_m2 with Docker Model Runner:
```
docker model run hf.co/kainatq/KaidenRp2400_12b_v1_m2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KaidenRp2400_12b_v1_m2

Merged using mergekit.

Merge Configuration

merge_method: dare_ties
base_model: mistralai/Mistral-Nemo-Base-2407
tokenizer_source: union
parameters:
  density: 0.5
  weight: 1.0
models:
  - model: mergekit-community/MN-Sappho-g2-12B
    parameters:
      weight: 0.33
  - model: nbeerbower/Nemoties-ChatML-12B
    parameters:
      weight: 0.33
  - model: pbevan11/Mistral-Nemo-Baseline-SFT
    parameters:
      weight: 0.34

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for kainatq/KaidenRp2400_12b_v1_m2

Quantizations

2 models