Instructions to use saishf/Kuro-Lotus-10.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Kuro-Lotus-10.7B with Transformers:

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

pipe = pipeline("text-generation", model="saishf/Kuro-Lotus-10.7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saishf/Kuro-Lotus-10.7B")
model = AutoModelForCausalLM.from_pretrained("saishf/Kuro-Lotus-10.7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use saishf/Kuro-Lotus-10.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saishf/Kuro-Lotus-10.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishf/Kuro-Lotus-10.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/saishf/Kuro-Lotus-10.7B

SGLang

How to use saishf/Kuro-Lotus-10.7B 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 "saishf/Kuro-Lotus-10.7B" \
    --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": "saishf/Kuro-Lotus-10.7B",
		"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 "saishf/Kuro-Lotus-10.7B" \
        --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": "saishf/Kuro-Lotus-10.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use saishf/Kuro-Lotus-10.7B with Docker Model Runner:
```
docker model run hf.co/saishf/Kuro-Lotus-10.7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Himitsui/KuroMitsu-11B
        layer_range: [0, 48]
      - model: BlueNipples/SnowLotus-v2-10.7B
        layer_range: [0, 48]
merge_method: slerp
base_model: Himitsui/KuroMitsu-11B
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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.90
AI2 Reasoning Challenge (25-Shot)	68.69
HellaSwag (10-Shot)	87.51
MMLU (5-Shot)	66.64
TruthfulQA (0-shot)	58.27
Winogrande (5-shot)	84.21
GSM8k (5-shot)	66.11

Downloads last month: 63

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for saishf/Kuro-Lotus-10.7B

BlueNipples/SnowLotus-v2-10.7B

Himitsui/KuroMitsu-11B

Merge model

this model

Merges

4 models

Quantizations

4 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.690
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.510
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

66.640
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

58.270
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.210
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.110