Text Generation
Transformers
Safetensors
llama
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
text-generation-inference
Inference Endpoints
Tiny-Llama-Llama-Dolphin-laser-1b-merge
Tiny-Llama-Llama-Dolphin-laser-1b-merge is a merge of the following models using 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
𧩠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
π» 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"])
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