Instructions to use johannhartmann/Obazda2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use johannhartmann/Obazda2 with Transformers:

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

pipe = pipeline("text-generation", model="johannhartmann/Obazda2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("johannhartmann/Obazda2")
model = AutoModelForCausalLM.from_pretrained("johannhartmann/Obazda2")
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 johannhartmann/Obazda2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "johannhartmann/Obazda2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "johannhartmann/Obazda2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/johannhartmann/Obazda2

SGLang

How to use johannhartmann/Obazda2 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 "johannhartmann/Obazda2" \
    --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": "johannhartmann/Obazda2",
		"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 "johannhartmann/Obazda2" \
        --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": "johannhartmann/Obazda2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use johannhartmann/Obazda2 with Docker Model Runner:
```
docker model run hf.co/johannhartmann/Obazda2
```

obazda2

This is a merge of pre-trained language models created using mergekit.

Benchmark

This model has not been dpo-aligned yet.

{
    "first_turn": 6.79375,
    "second_turn": 6.275,
    "categories": {
        "writing": 7.5,
        "roleplay": 7.65,
        "reasoning": 5.45,
        "math": 2.55,
        "coding": 4.8,
        "extraction": 7.2,
        "stem": 8.275,
        "humanities": 8.85
    },
    "average": 6.534375000000001
}

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mistral-7B-Instruct-v0.2 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistralai/Mistral-7B-Instruct-v0.2
    # no parameters necessary for base model
  - model: yam-peleg/Experiment26-7B
    parameters:
      density: 0.60
      weight: 0.30
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo
    parameters:
      density: 0.65
      weight: 0.40
  - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  int8_mask: true
tokenizer_source: union

dtype: bfloat16
random_seed: 42