Instructions to use gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16")
model = AutoModelForCausalLM.from_pretrained("gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16

SGLang

How to use gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16 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 "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16" \
    --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": "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16",
		"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 "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16" \
        --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": "gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16 with Docker Model Runner:
```
docker model run hf.co/gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fimbulvetr-V2-Kuro-Lotus

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:

D:\MK\Models\Fimbulvetr-11B-v2
D:\MK\Models\Kuro-Lotus-10.7B

Original Models

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: D:\MK\Models\Kuro-Lotus-10.7B
      layer_range: [0, 48]
    - model: D:\MK\Models\Fimbulvetr-11B-v2
      layer_range: [0, 48]
merge_method: slerp
base_model: D:\MK\Models\Kuro-Lotus-10.7B
parameters:
  t:
    - filter: self_attn
      value: [0.6, 0.7, 0.8, 0.9, 1]
    - filter: mlp
      value: [0.4, 0.3, 0.2, 0.1, 0]
    - value: 0.5
dtype: bfloat16

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Safetensors

Model size

11B params

Tensor type

BF16

Model tree for gelukuMLG/Fimbulvetr-V2-Kuro-Lotus-bf16

Quantizations

2 models