File size: 1,463 Bytes
34af77a 6f54856 34af77a 09cee02 b48a5bf 09cee02 7feaac2 09cee02 34af77a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
library_name: peft
base_model: openlm-research/open_llama_3b
---
# OpenLLaMa 3B PersonaChat
This is a LoRA finetune of [OpenLLaMa 3B](https://huggingface.co/openlm-research/open_llama_3b) on the [personachat-truecased](https://huggingface.co/datasets/bavard/personachat_truecased) dataset with 3 epochs of 500 steps.
## Use
Before using this model, you must first add these extra tokens:
```python
tokenizer.add_special_tokens({"additional_special_tokens": ["<|human|>", "<|bot|>", "<|endoftext|>"]})
model.resize_token_embeddings(len(tokenizer))
```
The model is finetuned with the format is as follows:
```
Personality:
- [...]
- [...]
<|human|>Hi there!<|endoftext|><|bot|>Hello!<|endoftext|>
```
To use this model, you must first define the personalities.
```python
personalities = """Personality:
- [...]
- [...]
"""
```
Then, follow the format:
```python
user = input(">>> ")
prompt = f"{personalities}<|human|>{user}<|endoftext|><|bot|>"
```
## Naming Format
[model name]-finetuned-[dataset]-e[number of epochs]-s[number of steps]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|