vicgalle/franken-SOLAR-18B-v1.0
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.
Evaluations coming soon!
This model has very good writing capabilities (compared to SOLAR-10.7B), specially for role-playing.
Quantized GGUF variants here https://huggingface.co/vicgalle/franken-SOLAR-18B-v1.0-GGUF
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
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])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 67.03 |
| AI2 Reasoning Challenge (25-Shot) | 65.53 |
| HellaSwag (10-Shot) | 86.45 |
| MMLU (5-Shot) | 63.72 |
| TruthfulQA (0-shot) | 62.14 |
| Winogrande (5-shot) | 78.53 |
| GSM8k (5-shot) | 45.79 |