Wiederchat-7b
Wiederchat-7b is a merge of the following models using LazyMergekit:
- mlabonne/OmniTruthyBeagle-7B-v0
- mayflowergmbh/Wiedervereinigung-7b-dpo-laser
- cognitivecomputations/openchat-3.5-0106-laser
Benchmark mt-bench-de
Even before dpo-alignment this model performs quite good:
{
"first_turn": 7.46875,
"second_turn": 6.7875,
"categories": {
"writing": 8.55,
"roleplay": 8,
"reasoning": 5.3,
"math": 4.35,
"coding": 4.6,
"extraction": 8.4,
"stem": 8.575,
"humanities": 9.25
},
"average": 7.128125
}
🧩 Configuration
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: mlabonne/OmniTruthyBeagle-7B-v0
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
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johannhartmann/Wiederchat-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"])