cstr_-_llama3-8b-spaetzle-v20-gguf

Quantization made by Richard Erkhov.

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llama3-8b-spaetzle-v20 - GGUF

• Model creator: https://huggingface.co/cstr/
• Original model: https://huggingface.co/cstr/llama3-8b-spaetzle-v20/

Original model description:

tags:

• merge
• mergekit
• lazymergekit
• nbeerbower/llama-3-wissenschaft-8B-v2 base_model:
• nbeerbower/llama-3-wissenschaft-8B-v2 license: llama3 language:
• en
• de

---

llama3-8b-spaetzle-v20

llama3-8b-spaetzle-v20 is a merge of the following models:

• cstr/llama3-8b-spaetzle-v13
• nbeerbower/llama-3-wissenschaft-8B-v2

Benchmarks

On EQ-Bench v2_de it achieves 65.7 (171/171 parseable). From Open LLM Leaderboard (details):

🧩 Configuration

models:
  - model: cstr/llama3-8b-spaetzle-v13
    # no parameters necessary for base model
  - model: nbeerbower/llama-3-wissenschaft-8B-v2
    parameters:
      density: 0.65
      weight: 0.4        
merge_method: dare_ties
base_model: cstr/llama3-8b-spaetzle-v13
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/llama3-8b-spaetzle-v20"
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"])