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cstr
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Model Details
Model Provider
cstr
Model Tree
HeshamHaroon/Arabic-llama3
mlabonne/NeuralDaredevil-8B-abliterated
Input Modalities
Output Modalities
Supported Functionality
yaml
models: - model: cstr/llama3-8b-spaetzle-v33 # no parameters necessary for base model - model: HeshamHaroon/Arabic-llama3 parameters: density: 0.65 weight: 0.4 - model: mlabonne/NeuralDaredevil-8B-abliterated parameters: density: 0.65 weight: 0.2 merge_method: dare_tiesbase_model: cstr/llama3-8b-spaetzle-v33parameters: int8_mask: truedtype: bfloat16random_seed: 0tokenizer_source: basepython
!pip install -qU transformers accelerate from transformers import AutoTokenizerimport transformersimport torch model = "cstr/llama3-8b-spaetzle-v37"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"])Added 2026-08-02 during an account-wide provenance review.
This is a model merge, not a format conversion. Most cstr/* repositories
are GGUF conversions, where the upstream research team remains the provider of
the model and the conversion changes only the numeric representation of the
weights. A merge produces a model that did not previously exist, so under
Regulation (EU) 2024/1689 the maintainer of this repository is plausibly the
provider of it, and the duties that survive the Art. 53(2)
free-and-open-source exemption — Art. 53(1)(c) and 53(1)(d) — attach here rather
than upstream.
Art. 53(1)(c) — copyright policy. No training corpus was assembled by this repository. Merging combines weights that other providers already published; it performs no text or data mining, so no rights reservation under Art. 4(3) of Directive (EU) 2019/790 was engaged by this step. Copyright questions arising from how the constituent models were themselves trained attach to their respective providers. Any credible claim that this repository redistributes material it has no right to redistribute will be acted on — contact via the Community tab.
Art. 53(1)(d) — training content. No data was used to train this model: it is a weight-space combination of models trained by others, and its training content is theirs. All 2 constituent models this card names are still published, so the chain can be followed from here.
models: - model: cstr/llama3-8b-spaetzle-v33 # no parameters necessary for base model - model: HeshamHaroon/Arabic-llama3 parameters: density: 0.65 weight: 0.4 - model: mlabonne/NeuralDaredevil-8B-abliterated parameters: density: 0.65 weight: 0.2 merge_method: dare_tiesbase_model: cstr/llama3-8b-spaetzle-v33parameters: int8_mask: truedtype: bfloat16random_seed: 0tokenizer_source: base!pip install -qU transformers accelerate from transformers import AutoTokenizerimport transformersimport torch model = "cstr/llama3-8b-spaetzle-v37"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"])