metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - FelixChao/WestSeverus-7B-DPO-v2
  - mayflowergmbh/Wiedervereinigung-7b-dpo-laser
  - cognitivecomputations/openchat-3.5-0106-laser
  - 🥨
  - 🍻
base_model:
  - FelixChao/WestSeverus-7B-DPO-v2
  - mayflowergmbh/Wiedervereinigung-7b-dpo-laser
  - cognitivecomputations/openchat-3.5-0106-laser
license: apache-2.0
language:
  - de

🥨 Brezn-7B

This is right now our best performing german speaking 7B model with an apache license, with an average of 7.49 on mt-bench-de. You can test this model here: mayflowergmbh/Brezn-7B-GGUF-Chat.

Brezn-7B is a dpo aligned merge of the following models using LazyMergekit:

💻 Usage

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("mayflowergmbh/Brezn-7b")
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Brezn-7b")

messages = [
    {"role": "user", "content": "Was ist dein Lieblingsgewürz??"},
    {"role": "assistant", "content": "Nun, ich mag besonders gerne einen guten Spritzer frischen Zitronensaft. Er fügt genau die richtige Menge an würzigem Geschmack hinzu, egal was ich gerade in der Küche zubereite!"},
    {"role": "user", "content": "Hast du Mayonnaise-Rezepte?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

mt-bench-de

{
    "first_turn": 7.6625,
    "second_turn": 7.31875,
    "categories": {
        "writing": 8.75,
        "roleplay": 8.5,
        "reasoning": 6.1,
        "math": 5.05,
        "coding": 5.4,
        "extraction": 7.975,
        "stem": 9,
        "humanities": 9.15
    },
    "average": 7.490625
}

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: FelixChao/WestSeverus-7B-DPO-v2
    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