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1models:
2 - model: LeoLM/leo-mistral-hessianai-7b-chat
3 # no parameters necessary for base model
4 - model: FelixChao/WestSeverus-7B-DPO-v2
5 parameters:
6 density: 0.60
7 weight: 0.30
8 - model: mayflowergmbh/Wiedervereinigung-7b-dpo
9 parameters:
10 density: 0.65
11 weight: 0.40
12 - model: cognitivecomputations/openchat-3.5-0106-laser
13 parameters:
14 density: 0.6
15 weight: 0.3
16merge_method: dare_ties
17base_model: LeoLM/leo-mistral-hessianai-7b-chat
18parameters:
19 int8_mask: true
20dtype: bfloat16
21random_seed: 0
22tokenizer_source: base1!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "cstr/Spaetzle-v61-7b"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])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.apache-2.0. All four constituents named in the mergekit config above (LeoLM/leo-mistral-hessianai-7b-chat, FelixChao/WestSeverus-7B-DPO-v2, mayflowergmbh/Wiedervereinigung-7b-dpo, cognitivecomputations/openchat-3.5-0106-laser) are Apache-2.0.