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 / README.md

johannhartmann

Update README.md

fbe9785 verified over 2 years ago

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	---
	base_model:
	- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
	- yam-peleg/Experiment26-7B
	- mistralai/Mistral-7B-Instruct-v0.2
	- mayflowergmbh/Wiedervereinigung-7b-dpo
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: apache-2.0
	language:
	- de
	---
	# obazda2

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	# Benchmark
	This model has not been dpo-aligned yet.
	```json
	{
	"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](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as a base.

	### Models Merged

	The following models were included in the merge:
	* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
	* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
	* [mayflowergmbh/Wiedervereinigung-7b-dpo](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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

	```