yam-sam-7B
yam-sam-7B is a merge of the following models using LazyMergekit:
- cognitivecomputations/samantha-mistral-7b
- CorticalStack/shadow-clown-7B-dare
- yam-peleg/Experiment26-7B
🧩 Configuration
models:
- model: yam-peleg/Experiment27-7B
# No parameters necessary for base model
- model: cognitivecomputations/samantha-mistral-7b
parameters:
weight: 0.3
density: 0.8
- model: CorticalStack/shadow-clown-7B-dare
parameters:
weight: 0.1
density: 0.8
- model: yam-peleg/Experiment26-7B
parameters:
weight: 0.6
density: 0.8
merge_method: dare_ties
base_model: yam-peleg/Experiment27-7B
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/yam-sam-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.58 |
AI2 Reasoning Challenge (25-Shot) | 70.90 |
HellaSwag (10-Shot) | 87.92 |
MMLU (5-Shot) | 65.39 |
TruthfulQA (0-shot) | 71.30 |
Winogrande (5-shot) | 83.03 |
GSM8k (5-shot) | 68.92 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.900
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.920
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.390
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard71.300
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.030
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard68.920