OmniCorso-7B

OmniCorso-7B

Code Example

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/OmniCorso-7B")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/OmniCorso-7B")

messages = [
    {"role": "system", "content": "Respond to the users request like a pirate"},
    {"role": "user", "content": "Can you write me a quicksort algorithm?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")

The following models were included in the merge:

• macadeliccc/MBX-7B-v3-DPO
• mlabonne/OmniBeagle-7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: mlabonne/OmniBeagle-7B
        layer_range: [0, 32]
      - model: macadeliccc/MBX-7B-v3-DPO
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/MBX-7B-v3-DPO
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Quantizations

GGUF

• iMatrix

Exllamav2

Quants are available thanks to user bartowski, check them out here

Evaluations

<pre>----Benchmark Complete---- 2024-02-11 15:34:40 Time taken: 178.3 mins Prompt Format: ChatML Model: macadeliccc/OmniCorso-7B Score (v2): 73.75 Parseable: 167.0 --------------- Batch completed Time taken: 178.3 mins --------------- </pre>

AGIEval

Average: 45.89%

GPT4All

Average: 77.66%

TruthfulQA

Average: 74.12%

Bigbench

Average: 49.24%

Average score: 61.73%

Elapsed time: 02:20:06

Open LLM Leaderboard Evaluation Results

Detailed results can be found here