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In my current testing RolePlayLake is Better than Silicon_Maid in RP and More Uncensored Than WestLakeI would try to only merge Uncensored Models with Baising towards Chat rather than Instruct 1slices:
2 - sources:
3 - model: SanjiWatsuki/Silicon-Maid-7B
4 layer_range: [0, 32]
5 - model: senseable/WestLake-7B-v2
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: senseable/WestLake-7B-v2
9parameters:
10 t:
11 - filter: self_attn
12 value: [0, 0.5, 0.3, 0.7, 1]
13 - filter: mlp
14 value: [1, 0.5, 0.7, 0.3, 0]
15 - value: 0.5
16dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "fhai50032/RolePlayLake-7B"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])| Metric | Value |
|---|---|
| Avg. | 72.54 |
| AI2 Reasoning Challenge (25-Shot) | 70.56 |
| HellaSwag (10-Shot) | 87.42 |
| MMLU (5-Shot) | 64.55 |
| TruthfulQA (0-shot) | 64.38 |
| Winogrande (5-shot) | 83.27 |
| GSM8k (5-shot) | 65.05 |