SOLAR-10.7B-Instruct-ties

SOLAR-10.7B-Instruct-ties is a merge of the following models using mergekit:

• kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
• VAGOsolutions/SauerkrautLM-SOLAR-Instruct

🧩 Configuration

models:
  - model: upstage/SOLAR-10.7B-Instruct-v1.0
    # no parameters necessary for base model
  - model: kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
    parameters:
      density: 0.5
      weight: 0.5
  - model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
  normalize: true
dtype: float16

💻 Example Python Code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "nfaheem/SOLAR-10.7B-Instruct-ties"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

📋 Summary Eval:

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
74.24	70.9	88.58	66.34	71.88	83.5	64.06

📈 Huggingface Leaderboard