cstr / Spaetzle-v12-7b

Spaetzle-v12-7b

Spaetzle-v12-7b is a merge of the following models using LazyMergekit :

• flemmingmiguel/NeuDist-Ro-7B
• Blizado/discolm-mfto-7b-german-v0.1
• ResplendentAI/Flora_DPO_7B
• on the basis of mayflowergmbh/Wiedervereinigung-7b-dpo-laser

As expected, this is a little bit worse in general English tasks over cstr/spaetzle-v8-7b , but a tiny little bit better on German tasks, at least some: e.g. it reaches an EQ-Bench (de) score of 64.81, but only

AGIEval

Average: 42.64%

GPT4All

Average: 74.3%

TruthfulQA

Average: 58.44%

Bigbench

Average: 44.44%

Average score: 54.95%

Elapsed time: 02:50:51

🧩 Configuration

models:
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    # no parameters necessary for base model
  - model: flemmingmiguel/NeuDist-Ro-7B
    parameters:
      density: 0.60
      weight: 0.30
  - model: Blizado/discolm-mfto-7b-german-v0.1
    parameters:
      density: 0.65
      weight: 0.40
  - model: ResplendentAI/Flora_DPO_7B
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v12-7b"
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"])