FuseChat: Knowledge Fusion of Chat Models
Paper • 2408.07990 • Published • 15
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TareksTesting/UNNAMED-MODEL-2C")
model = AutoModelForCausalLM.from_pretrained("TareksTesting/UNNAMED-MODEL-2C")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))This is a merge of pre-trained language models created using mergekit.
This model was merged using the SCE merge method using TareksLab/Scrivener-Base-V6-LLaMA-70B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: TareksLab/Wordsmith-V7-LLaMa-70B
parameters:
select_topk: 0.16
- model: TareksLab/Anathema-V8-LLaMA-70B
parameters:
select_topk: 0.16
- model: TareksLab/Scrivener-Base-V6-LLaMA-70B
parameters:
select_topk: 0.16
- model: TareksLab/RolePlayer-V6-LLaMa-70B
parameters:
select_topk: 0.16
- model: TareksLab/Cortex-V4-LLaMA-70B
parameters:
select_topk: 0.16
merge_method: sce
base_model: TareksLab/Scrivener-Base-V6-LLaMA-70B
dtype: bfloat16
chat_template: llama3
tokenizer:
source: TareksLab/Cortex-V4-LLaMA-70B
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TareksTesting/UNNAMED-MODEL-2C") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)