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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- persimmon
---
# perSLIMmon-8b-base
> persimmon-8b went to the vocab lipo clinic
A slimmed-down version of [persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) which removes the ~70,000 unused entries in the model vocabulary and tokenizer (see the safetensors layer overview). Should be _slightly_ faster.
Credit: [fine-tune-fuyu](https://github.com/phillip-kravtsov/fine-tune-fuyu) (`scripts/surgery.py` was adapted for persimmon)
## inference
install required pkgs:
```sh
pip install -U transformers accelerate bitsandbytes sentencepiece
```
load in 4bit & run inference:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("pszemraj/perSLIMmon-8b-base")
model = AutoModelForCausalLM.from_pretrained(
"pszemraj/perSLIMmon-8b-base",
load_in_4bit=True, # GPU required
torch_dtype="auto",
device_map="auto",
)
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(
model.device
)
tokens = model.generate(
**inputs,
max_new_tokens=64,
temperature=0.75,
top_p=0.95,
epsilon_cutoff=1e-5,
repetition_penalty=1.05,
renormalize_logits=True,
do_sample=True,
) # adapt inference params as needed
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```
inference is decently fast on a colab T4:
```
CPU times: user 6.01 s, sys: 138 ms, total: 6.15 s
Wall time: 6.23 s
```
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