Instructions to use seedboxai/KafkaLM-Mixtral-8x7B-V0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seedboxai/KafkaLM-Mixtral-8x7B-V0.2 with Transformers:

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

pipe = pipeline("text-generation", model="seedboxai/KafkaLM-Mixtral-8x7B-V0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("seedboxai/KafkaLM-Mixtral-8x7B-V0.2")
model = AutoModelForMultimodalLM.from_pretrained("seedboxai/KafkaLM-Mixtral-8x7B-V0.2")
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 seedboxai/KafkaLM-Mixtral-8x7B-V0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seedboxai/KafkaLM-Mixtral-8x7B-V0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seedboxai/KafkaLM-Mixtral-8x7B-V0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/seedboxai/KafkaLM-Mixtral-8x7B-V0.2

SGLang

How to use seedboxai/KafkaLM-Mixtral-8x7B-V0.2 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 "seedboxai/KafkaLM-Mixtral-8x7B-V0.2" \
    --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": "seedboxai/KafkaLM-Mixtral-8x7B-V0.2",
		"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 "seedboxai/KafkaLM-Mixtral-8x7B-V0.2" \
        --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": "seedboxai/KafkaLM-Mixtral-8x7B-V0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use seedboxai/KafkaLM-Mixtral-8x7B-V0.2 with Docker Model Runner:
```
docker model run hf.co/seedboxai/KafkaLM-Mixtral-8x7B-V0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KafkaLM-8x7b-German-V0.1

KafkaLM 8x7b is a MoE model based on Mistral AI´s Mixtral 8x7b which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).

KafkaLM 8x7b is a Seedbox project trained by Dennis Dickmann.

Why Kafka? The models are proficient, yet creative, have some tendencies to linguistically push boundaries 😊

Model Details

The purpose of releasing the KafkaLM series is to contribute to the German AI community with a set of fine-tuned LLMs that are easy to use in everyday applications across a variety of tasks.

The main goal was to provide LLMs proficient in German, especially to be used in German-speaking business contexts where English alone is not sufficient.

DPO

The model has been aligned with a german and modified version of the ultra feedback dataset from huggingface.

Dataset

I used a 8k filtered version of the following seedboxai/multitask_german_examples_32k

Inference

Getting started with the model is straightforward

import transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "seedboxai/KafkaLM-Mixtral-8x7B-V0.2"

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)

pipeline = transformers.pipeline(
    model=model, tokenizer=tokenizer,
    return_full_text=True,  
    task='text-generation',
    device="cuda",
)

messages = [
    {"role": "system", "content": "Du bist ein hilfreicher KI-Assistent."},
    {"role": "user", "content": "Wer ist eigentlich dieser Kafka?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("</s>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=max_new_tokens,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

print(outputs[0]["generated_text"][len(prompt):])

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model.

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Safetensors

Model size

47B params

Tensor type

BF16

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Quantizations

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