Instructions to use ChaoticNeutrals/Kunocchini-7b-128k-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Kunocchini-7b-128k-test with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Kunocchini-7b-128k-test", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Kunocchini-7b-128k-test", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Kunocchini-7b-128k-test", trust_remote_code=True)
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

vLLM

How to use ChaoticNeutrals/Kunocchini-7b-128k-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/Kunocchini-7b-128k-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/Kunocchini-7b-128k-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/Kunocchini-7b-128k-test

SGLang

How to use ChaoticNeutrals/Kunocchini-7b-128k-test 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 "ChaoticNeutrals/Kunocchini-7b-128k-test" \
    --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": "ChaoticNeutrals/Kunocchini-7b-128k-test",
		"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 "ChaoticNeutrals/Kunocchini-7b-128k-test" \
        --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": "ChaoticNeutrals/Kunocchini-7b-128k-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ChaoticNeutrals/Kunocchini-7b-128k-test with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/Kunocchini-7b-128k-test
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Thanks to @Epiculous for the dope model/ help with llm backends and support overall.

Id like to also thank @kalomaze for the dope sampler additions to ST.

@SanjiWatsuki Thank you very much for the help, and the model!

ST users can find the TextGenPreset in the folder labeled so.

Quants: Thank You @s3nh! https://huggingface.co/s3nh/Kunocchini-7b-128k-test-GGUF and @bartowski https://huggingface.co/bartowski/Kunocchini-7b-128k-test-exl2 Thanks To @Lewdiculus for the Imatrix gguf quants: https://huggingface.co/Lewdiculous/Kunocchini-7b-128k-test-GGUF-Imatrix

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [0, 32]
      - model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
        layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.24
AI2 Reasoning Challenge (25-Shot)	66.98
HellaSwag (10-Shot)	85.62
MMLU (5-Shot)	61.27
TruthfulQA (0-shot)	59.35
Winogrande (5-shot)	77.90
GSM8k (5-shot)	52.31

Downloads last month: 83

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ChaoticNeutrals/Kunocchini-7b-128k-test

Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context

SanjiWatsuki/Kunoichi-DPO-v2-7B

Merge model

this model

Finetunes

1 model

Merges

19 models

Quantizations

2 models

Evaluation results

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

66.980
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.620
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

61.270
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

59.350
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.900
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

52.310