metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - TinyLlama/TinyLlama-1.1B-Chat-v1.0
  - cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
  - cognitivecomputations/TinyDolphin-2.8.1-1.1b
  - TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
base_model:
  - TinyLlama/TinyLlama-1.1B-Chat-v1.0
  - cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
  - cognitivecomputations/TinyDolphin-2.8.1-1.1b
  - TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T

Tiny-Llama-Llama-Dolphin-laser-1b-merge

Tiny-Llama-Llama-Dolphin-laser-1b-merge is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    parameters:
      weight: 1.0
  - model: cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
    parameters:
      weight: 1.0
  - model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
    parameters:
      weight: 0.4
  - model: TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
    parameters:
      weight: 0.6
merge_method: linear
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge"
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"])