Instructions to use KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b with Transformers:

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

pipe = pipeline("text-generation", model="KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b")
model = AutoModelForCausalLM.from_pretrained("KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b", 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 KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b

SGLang

How to use KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b 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 "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b" \
    --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": "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b",
		"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 "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b" \
        --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": "KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b with Docker Model Runner:
```
docker model run hf.co/KaraKaraWarehouse/LLENN-v0.75-Qwen2.5-72b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLENN-v0.75-Qwen2.5-72b

I liked the previous model, but didn't exactly liked the claude vibes it's giving me. So I removed magnum. Other than that, there isn't any new model to merge in so the rest is kept as-is.

Please do not ask for quants, contact others instead.

All models are ready for testing on featherless.ai as soon as it goes live.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.0
  - model: ZeusLabs/Chronos-Platinum-72B
  - model: abacusai/Dracarys2-72B-Instruct
  - model: rombodawg/Rombos-LLM-V2.5-Qwen-72b
  - model: m8than/banana-2-b-72b

merge_method: model_stock
base_model: Qwen/Qwen2.5-72B
parameters:
  normalize: true
dtype: bfloat16