README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - timpal0l/Mistral-7B-v0.1-flashback-v2
  - EmbeddedLLM/Mistral-7B-Merge-14-v0.2
  - Nexusflow/Starling-LM-7B-beta
base_model:
  - timpal0l/Mistral-7B-v0.1-flashback-v2
  - EmbeddedLLM/Mistral-7B-Merge-14-v0.2
  - Nexusflow/Starling-LM-7B-beta
license: apache-2.0

SwedishBeagle-dare

SwedishBeagle-dare is a merge of the following models using LazyMergekit:

• timpal0l/Mistral-7B-v0.1-flashback-v2
• EmbeddedLLM/Mistral-7B-Merge-14-v0.2
• Nexusflow/Starling-LM-7B-beta

🧩 Configuration

models:
  - model: mlabonne/NeuralBeagle14-7B
    # No parameters necessary for base model
  - model: timpal0l/Mistral-7B-v0.1-flashback-v2
    parameters:
      density: 0.53
      weight: 0.3
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
    parameters:
      density: 0.53
      weight: 0.4
  - model: Nexusflow/Starling-LM-7B-beta
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "FredrikBL/SwedishBeagle-dare"
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"])