Instructions to use Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2")
model = AutoModelForCausalLM.from_pretrained("Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2", 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 Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2

SGLang

How to use Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2 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 "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2" \
    --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": "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2",
		"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 "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2" \
        --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": "Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2
```

Kunokukulemonchini-7b-7.1bpw-exl2

This is an 7.1 bpw exl2 quant of a merger icefog72/Kunokukulemonchini-7b.

I wanted to replicate what IceFog did with 6GB cards, looking for long context and quality but scaling it to 8GB cards.

Works great for people with 8GB of vram who are looking for both long context and quality.

With a 4060 8GB i end up getting 16k context and better quality responces compared to the 6.5bpw version.

Merge Details

Slightly edited kukulemon-7B config.json before merge to get at least ~32k context window.

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: grimjim/kukulemon-7B
        layer_range: [0, 32]
      - model: Nitral-AI/Kunocchini-7b-128k-test
        layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Kunocchini-7b-128k-test
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: float16

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Model tree for Natkituwu/Kunokukulemonchini-7b-7.1bpw-exl2

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grimjim/kukulemon-7B

Merge model

this model