Instructions to use mayflowergmbh/Wiederchat-7b-dpo-laser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayflowergmbh/Wiederchat-7b-dpo-laser with Transformers:

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

pipe = pipeline("text-generation", model="mayflowergmbh/Wiederchat-7b-dpo-laser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiederchat-7b-dpo-laser")
model = AutoModelForMultimodalLM.from_pretrained("mayflowergmbh/Wiederchat-7b-dpo-laser")
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 mayflowergmbh/Wiederchat-7b-dpo-laser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mayflowergmbh/Wiederchat-7b-dpo-laser

SGLang

How to use mayflowergmbh/Wiederchat-7b-dpo-laser 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 "mayflowergmbh/Wiederchat-7b-dpo-laser" \
    --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": "mayflowergmbh/Wiederchat-7b-dpo-laser",
		"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 "mayflowergmbh/Wiederchat-7b-dpo-laser" \
        --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": "mayflowergmbh/Wiederchat-7b-dpo-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mayflowergmbh/Wiederchat-7b-dpo-laser with Docker Model Runner:
```
docker model run hf.co/mayflowergmbh/Wiederchat-7b-dpo-laser
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wiederchat-7b-dpo-laser

Wiederchat-7b-dpo is a laser-qlorad dpo-aligned merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: mlabonne/OmniTruthyBeagle-7B-v0
    parameters:
      density: 0.60
      weight: 0.30
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    parameters:
      density: 0.65
      weight: 0.40
  - model: cognitivecomputations/openchat-3.5-0106-laser
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

📈 Mt-Bench-De

{
    "first_turn": 7.8875,
    "second_turn": 7.31875,
    "categories": {
        "writing": 8.65,
        "roleplay": 8.225,
        "reasoning": 6.5,
        "math": 4.55,
        "coding": 6.1,
        "extraction": 8.25,
        "stem": 9.2,
        "humanities": 9.35
    },
    "average": 7.603125
}

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johannhartmann/Wiederchat-7b-dpo-laser"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])