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1slices:
2 - sources:
3 - model: Nexusflow/Starling-LM-7B-beta
4 layer_range: [0, 32]
5 - model: FuseAI/FuseChat-7B-VaRM
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: FuseAI/FuseChat-7B-VaRM
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 = "Artples/M-LChat-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. | 69.57 |
| AI2 Reasoning Challenge (25-Shot) | 65.61 |
| HellaSwag (10-Shot) | 84.59 |
| MMLU (5-Shot) | 65.44 |
| TruthfulQA (0-shot) | 50.94 |
| Winogrande (5-shot) | 81.37 |
| GSM8k (5-shot) | 69.45 |
| Metric | Value |
|---|---|
| Avg. | 21.02 |
| IFEval (0-Shot) | 52.97 |
| BBH (3-Shot) | 24.20 |
| MATH Lvl 5 (4-Shot) | 7.93 |
| GPQA (0-shot) | 7.38 |
| MuSR (0-shot) | 8.12 |
| MMLU-PRO (5-shot) | 25.54 |