--- base_model: - upstage/SOLAR-10.7B-Instruct-v1.0 - NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - mergekit - merge - solar - gguf license: apache-2.0 --- # vicgalle/franken-SOLAR-18B-v1.0-GGUF This is a SOLAR-like model upscaled to 18B. It is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. This repo has the quantized GGUF versions from https://huggingface.co/vicgalle/franken-SOLAR-18B-v1.0 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5fad8602b8423e1d80b8a965/mMyHMuuftG71_o4at5suy.png) Evaluations coming soon! This model has very good writing capabilities (compared to SOLAR-10.7B), specially for role-playing. ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) * [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [0, 12] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [6, 18] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [13, 25] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [19, 31] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [26, 38] - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [32, 44] - sources: - model: NousResearch/Nous-Hermes-2-SOLAR-10.7B layer_range: [39, 48] merge_method: passthrough dtype: float16 ``` ### Usage You can use the provided template: ``` tokenizer = AutoTokenizer.from_pretrained("vicgalle/franken-SOLAR-18B-v1.0") model = AutoModelForCausalLM.from_pretrained("vicgalle/franken-SOLAR-18B-v1.0", torch_dtype=torch.float16, load_in_4bit=True) conversation = [ {'role': 'system', 'content': SYSTEM_PROMPT}, {'role': 'user', 'content': USER_PROMPT} ] prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, use_cache=True, max_new_tokens=1024, do_sample=True, temperature=0.8) output_text = tokenizer.decode(outputs[0]) ```