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Model: RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf
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Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-typhoon-v1.5-8b-instruct - GGUF
- Model creator: https://huggingface.co/scb10x/
- Original model: https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3-typhoon-v1.5-8b-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q2_K.gguf) | Q2_K | 2.96GB |
| [llama-3-typhoon-v1.5-8b-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [llama-3-typhoon-v1.5-8b-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [llama-3-typhoon-v1.5-8b-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q3_K.gguf) | Q3_K | 3.74GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [llama-3-typhoon-v1.5-8b-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q4_0.gguf) | Q4_0 | 4.34GB |
| [llama-3-typhoon-v1.5-8b-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q4_K.gguf) | Q4_K | 4.58GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q4_1.gguf) | Q4_1 | 4.78GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q5_0.gguf) | Q5_0 | 5.21GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q5_K.gguf) | Q5_K | 5.34GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q5_1.gguf) | Q5_1 | 5.65GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q6_K.gguf) | Q6_K | 6.14GB |
| [llama-3-typhoon-v1.5-8b-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5-8b-instruct-gguf/blob/main/llama-3-typhoon-v1.5-8b-instruct.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
license: llama3
language:
- en
- th
pipeline_tag: text-generation
tags:
- instruct
- chat
---
**Llama-3-Typhoon-v1.5-8B: Thai Large Language Model (Instruct)**
**Llama-3-Typhoon-v1.5-8B-instruct** is a *instruct* Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B.
![Typhoon 1.5 8b benchmark](https://storage.googleapis.com/typhoon-public/assets/1.5-8b-benchmark.png)
For release post, please see our [blog](https://blog.opentyphoon.ai/typhoon-1-5-release-a9364cb8e8d7).
*To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name.
## **Model Description**
- **Model type**: A 8B instruct decoder-only model based on Llama architecture.
- **Requirement**: transformers 4.38.0 or newer.
- **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧
- **License**: [Llama 3 Community License](https://llama.meta.com/llama3/license/)
## **Performance**
| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam | MMLU |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Typhoon-1.0 (Mistral) | 0.379 | 0.393 | 0.700 | 0.414 | 0.324 | 0.442 | 0.391 | 0.547 |
| Typhoon-1.5 8B (Llama3) | ***0.446*** | ***0.431*** | ***0.722*** | ***0.526*** | ***0.407*** | ***0.506*** | ***0.460*** | ***0.614*** |
| Sailor 7B | 0.372 | 0.379 | 0.678 | 0.405 | 0.396 | 0.446 | 0.411 | 0.553 |
| SeaLLM 2.0 7B | 0.327 | 0.311 | 0.656 | 0.414 | 0.321 | 0.406 | 0.354 | 0.579 |
| OpenThaiGPT 1.0.0 7B | 0.238 | 0.249 | 0.444 | 0.319 | 0.289 | 0.308 | 0.268 | 0.369 |
| SambaLingo-Thai-Chat 7B | 0.251 | 0.241 | 0.522 | 0.302 | 0.262 | 0.316 | 0.309 | 0.388 |
## Usage Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "scb10x/llama-3-typhoon-v1.5-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful assistant who're always speak Thai."},
{"role": "user", "content": "ขอสูตรไก่ย่าง"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.4,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Chat Template
We use llama3 chat-template.
```python
{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}
```
## **Intended Uses & Limitations**
This model is an instructional model. However, its still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
## **Follow us**
**https://twitter.com/opentyphoon**
## **Support**
**https://discord.gg/CqyBscMFpg**
## **SCB10X AI Team**
- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
- If you find Typhoon-8B useful for your work, please cite it using:
```
@article{pipatanakul2023typhoon,
title={Typhoon: Thai Large Language Models},
author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
year={2023},
journal={arXiv preprint arXiv:2312.13951},
url={https://arxiv.org/abs/2312.13951}
}
```
## **Contact Us**
- General & Collaboration: **[kasima@scb10x.com](mailto:kasima@scb10x.com)**, **[pathomporn@scb10x.com](mailto:pathomporn@scb10x.com)**
- Technical: **[kunat@scb10x.com](mailto:kunat@scb10x.com)**

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