TinyLlama/TinyLlama-1.1B-Chat-v0.5-GGUF
Quantized GGUF model files for TinyLlama-1.1B-Chat-v0.5 from TinyLlama
| Name | Quant method | Size |
|---|---|---|
| tinyllama-1.1b-chat-v0.5.q2_k.gguf | q2_k | 482.15 MB |
| tinyllama-1.1b-chat-v0.5.q3_k_m.gguf | q3_k_m | 549.85 MB |
| tinyllama-1.1b-chat-v0.5.q4_k_m.gguf | q4_k_m | 667.82 MB |
| tinyllama-1.1b-chat-v0.5.q5_k_m.gguf | q5_k_m | 782.05 MB |
| tinyllama-1.1b-chat-v0.5.q6_k.gguf | q6_k | 903.42 MB |
| tinyllama-1.1b-chat-v0.5.q8_0.gguf | q8_0 | 1.17 GB |
Original Model Card:
TinyLlama-1.1B
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
This Model
This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T. The dataset used is OpenAssistant/oasst_top1_2023-08-25 following the chatml format.
How to use
You will need the transformers>=4.31 Do check the TinyLlama github page for more information.
from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-Chat-v0.5"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
CHAT_EOS_TOKEN_ID = 32002
prompt = "How to get in a good university?"
formatted_prompt = (
f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
)
sequences = pipeline(
formatted_prompt,
do_sample=True,
top_k=50,
top_p = 0.9,
num_return_sequences=1,
repetition_penalty=1.1,
max_new_tokens=1024,
eos_token_id=CHAT_EOS_TOKEN_ID,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")