🧩 Configuration
#slices:
# - sources:
# - model: liminerity/M7-7b
# layer_range: [0, 32]
# - model: AurelPx/Percival_01-7b-slerp
# layer_range: [0, 32]
#merge_method: slerp
#base_model: liminerity/M7-7b
#parameters:
# t:
# - filter: self_attn
# value: [0.729086620552417, 0.4742644222549576, 0.47065411083849984, 0.9373988134098882, 0.6820526568624088]
# - filter: mlp
# value: [0.270913379447583, 0.5257355777450424, 0.06260118659011182, 0.06260118659011182, 0.3179473431375912]
# - value: 0.8480269455484635
#dtype: bfloat16
#random_seed: 0
#slices:
# - sources:
# - model: psmathur/orca_mini_v3_13b
# layer_range: [0, 40]
# - model: garage-bAInd/Platypus2-7b
# layer_range: [0, 32]
#merge_method: slerp
#base_model: psmathur/orca_mini_v3_13b
#parameters:
# t:
# - filter: self_attn
# value: [0.729086620552417, 0.4742644222549576, 0.47065411083849984, 0.9373988134098882, 0.6820526568624088]
# - filter: mlp
# value: [0.270913379447583, 0.5257355777450424, 0.5293458891615002, 0.06260118659011182, 0.3179473431375912]
# - value: 0.8480269455484635
#dtype: float16
#random_seed: 0
#slices:
# - sources:
# - model: psmathur/orca_mini_v3_13b
# parameters:
# density: [1, 0.7, 0.1] # density gradient
# weight: 1.0
# - model: garage-bAInd/Platypus2-13B
# parameters:
# density: 0.5
# weight: [0, 0.3, 0.7, 1] # weight gradient
# - model: WizardLM/WizardMath-13B-V1.0
# parameters:
# density: 0.33
# weight:
# - filter: mlp
# value: 0.5
# - value: 0
#merge_method: ties
#base_model: TheBloke/Llama-2-13B-fp16
#parameters:
# normalize: true
# int8_mask: true
#dtype: float16
#random_seed: 0
base_model: mlabonne/AlphaMonarch-7B
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "EthanLiu1991/BioMedGPT"
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"])