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