108 lines
3.8 KiB
Markdown
108 lines
3.8 KiB
Markdown
---
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base_model:
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- cstr/llama3.1-8b-spaetzle-v59
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- cstr/llama3.1-8b-spaetzle-v63
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- cstr/llama3.1-8b-spaetzle-v66
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- cstr/llama3.1-8b-spaetzle-v73
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tags:
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- merge
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- mergekit
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license: llama3.1
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language:
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- en
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- de
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library_name: transformers
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---
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# llama3.1-8b-spaetzle-v74
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llama3.1-8b-spaetzle-v74 is a merge of the following models:
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* [cstr/llama3.1-8b-spaetzle-v59](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v59)
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* [cstr/llama3.1-8b-spaetzle-v63](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v63)
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* [cstr/llama3.1-8b-spaetzle-v66](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v66)
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* [cstr/llama3.1-8b-spaetzle-v73](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v73)
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EQ-Bench v2_de: 68.05 169/171, en: 75.27 - which is not the best, but it produces decent answers for some trick questions, and i have a sweet spot for that ;)
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## 🧩 Configuration
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```yamlmodels:
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models:
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- model: cstr/llama3.1-8b-spaetzle-v59
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parameters:
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weight: 0.3
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density: 0.5
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- model: cstr/llama3.1-8b-spaetzle-v63
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parameters:
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weight: 0.15
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density: 0.5
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- model: cstr/llama3.1-8b-spaetzle-v66
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parameters:
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weight: 0.15
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density: 0.5
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- model: cstr/llama3.1-8b-spaetzle-v73
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parameters:
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weight: 0.4
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density: 0.5
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base_model: cstr/llama3.1-8b-spaetzle-v59
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merge_method: della_linear
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parameters:
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int8_mask: true
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normalize: true
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epsilon: 0.1
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lambda: 1.0
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density: 0.7
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dtype: bfloat16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "cstr/llama3.1-8b-spaetzle-v74"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## EU AI Act Art. 53 — provider obligations
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Added 2026-08-02 during an account-wide provenance review.
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**This is a model merge, not a format conversion.** Most `cstr/*` repositories
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are GGUF conversions, where the upstream research team remains the provider of
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the model and the conversion changes only the numeric representation of the
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weights. A merge produces a model that did not previously exist, so under
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Regulation (EU) 2024/1689 the maintainer of this repository is plausibly the
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**provider** of it, and the duties that survive the Art. 53(2)
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free-and-open-source exemption — Art. 53(1)(c) and 53(1)(d) — attach here rather
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than upstream.
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**Art. 53(1)(c) — copyright policy.** No training corpus was assembled by this
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repository. Merging combines weights that other providers already published; it
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performs no text or data mining, so no rights reservation under Art. 4(3) of
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Directive (EU) 2019/790 was engaged by this step. Copyright questions arising
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from how the constituent models were themselves trained attach to their
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respective providers. Any credible claim that this repository redistributes
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material it has no right to redistribute will be acted on — contact via the
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Community tab.
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**Art. 53(1)(d) — training content.** No data was used to train this model: it
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is a weight-space combination of models trained by others, and its training
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content is theirs. Of the 4 constituent models this card names, **1 are still published and 3 are not**: `cstr/llama3.1-8b-spaetzle-v63`, `cstr/llama3.1-8b-spaetzle-v66`, `cstr/llama3.1-8b-spaetzle-v73`. For those, the training-content chain cannot be followed from this card, and no summary is reconstructed here in their place — an untraceable summary presented as a traceable one would be worse than the gap.
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