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1models:
2 - model: mlabonne/NeuralBeagle14-7B
3# no params for base model
4 - model: fhai50032/RolePlayLake-7B
5 parameters:
6 weight: 0.8
7 density: 0.6
8 - model: mlabonne/NeuralBeagle14-7B
9 parameters:
10 weight: 0.3
11 density: [0.1,0.3,0.5,0.7,1]
12merge_method: dare_ties
13base_model: mlabonne/NeuralBeagle14-7B
14parameters:
15 normalize: true
16 int8_mask: true
17dtype: float161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "fhai50032/BeagleLake-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. | 72.34 |
| AI2 Reasoning Challenge (25-Shot) | 70.39 |
| HellaSwag (10-Shot) | 87.38 |
| MMLU (5-Shot) | 64.25 |
| TruthfulQA (0-shot) | 64.92 |
| Winogrande (5-shot) | 83.19 |
| GSM8k (5-shot) | 63.91 |