Quantization made by Richard Erkhov.
TinyAiroboros-2.2.1 - AWQ
- Model creator: https://huggingface.co/aloobun/
- Original model: https://huggingface.co/aloobun/TinyAiroboros-2.2.1/
Original model description:
datasets: - jondurbin/airoboros-2.2.1 tags: - airoboros - tinyllama
This model is a fine-tuned version of PY007/TinyLlama-1.1B-Chat-v0.3 (finetuned on 15k rows of airoboros-2.2.1 dataset)
lm-eval
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.2671|± |0.0129|
| | |acc_norm|0.2850|± |0.0132|
|arc_easy | 0|acc |0.5673|± |0.0102|
| | |acc_norm|0.5109|± |0.0103|
|boolq | 1|acc |0.6040|± |0.0086|
|hellaswag | 0|acc |0.4155|± |0.0049|
| | |acc_norm|0.5420|± |0.0050|
|openbookqa | 0|acc |0.2200|± |0.0185|
| | |acc_norm|0.3420|± |0.0212|
|piqa | 0|acc |0.7057|± |0.0106|
| | |acc_norm|0.6970|± |0.0107|
|winogrande | 0|acc |0.5714|± |0.0139|
Usage:
from transformers import AutoTokenizer
import transformers
import torch
model = "aloobun/TinyAiroboros-2.2.1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = "Write a short story about a dystopian society."
sequences = pipeline(
f'[INST] {prompt} [/INST]',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=1024,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")