cstr/llama3-8b-spaetzle-v33-GGUF

llama3-8b-spaetzle-v33

These are GGUF quants of cstr/llama3-8b-spaetzle-v33, a merge of the following models:

• cstr/llama3-8b-spaetzle-v31
• cstr/llama3-8b-spaetzle-v28
• cstr/llama3-8b-spaetzle-v26
• cstr/llama3-8b-spaetzle-v20

It attempts a compromise in usefulness for German and English tasks.

It achieves on EQ-Bench v2_de as q4km quants 66.59 (171 of 171 parseable).

🧩 Configuration

models:
  - model: cstr/llama3-8b-spaetzle-v20
    # no parameters necessary for base model
  - model: cstr/llama3-8b-spaetzle-v31
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v28
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3-8b-spaetzle-v26
    parameters:
      density: 0.65
      weight: 0.15
merge_method: dare_ties
base_model: cstr/llama3-8b-spaetzle-v20
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-v33"
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"])