license, tags, base_model, model-index

license	tags	base_model	model-index
apache-2.0	mergekit merge solar	upstage/SOLAR-10.7B-Instruct-v1.0 NousResearch/Nous-Hermes-2-SOLAR-10.7B	name results franken-SOLAR-18B-v1.0 task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 65.53 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 86.45 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 63.72 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 62.14 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 78.53 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 45.79 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/franken-SOLAR-18B-v1.0 Open LLM Leaderboard

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:

• upstage/SOLAR-10.7B-Instruct-v1.0
• NousResearch/Nous-Hermes-2-SOLAR-10.7B

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