Instructions to use v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno with Transformers:

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

pipe = pipeline("text-generation", model="v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno")
model = AutoModelForCausalLM.from_pretrained("v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno

SGLang

How to use v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno 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 "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno" \
    --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": "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno",
		"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 "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno" \
        --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": "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno with Docker Model Runner:
```
docker model run hf.co/v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-14B-Gutenberg-Instruct-Slerpeno

GGUF from mradermacher!

GGUF from QuantFactory!

GGUF static

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. (sophosympatheia gradient)

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Qwen/Qwen2.5-14B-Instruct
merge_method: slerp
base_model: v000000/Qwen2.5-14B-Gutenberg-1e-Delta
parameters:
  t:
    - value: [0, 0, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0, 0]
dtype: bfloat16

The idea here is that Gutenberg DPO stays in the output/input 100% while merging smoothly with the base instruct model in the deeper layers to heal loss and increase intelligence.