File size: 1,797 Bytes
5276e4c ce87f00 5276e4c ce87f00 5276e4c ce87f00 |
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
datasets:
- airesearch/WangchanThaiInstruct
---
# Dataset
This model finetune on [airesearch/WangchanThaiInstruct](https://huggingface.co./datasets/airesearch/WangchanThaiInstruct)
`23 sep 2024`
Training details:
- epochs: 1
- learning rate: 2e-4
- learning rate scheduler type: linear
- Warmup ratio: 0.3
- cutoff len (i.e. context length): 2048
- global batch size: 8
- fine-tuning type: qlora
- optimizer: adamw_8bit
ps. 12 Hours from T4 Kaggle
# Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Konthee/Llama-3.1-8B-ThaiInstruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)
messages = [
{"role": "user", "content": "สอนภาษาไทยหน่อย"},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=8192,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
# Uploaded model
- **Developed by:** Konthee
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |