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Model: typhoon-ai/llama-3-typhoon-v1.5x-8b-instruct Source: Original Platform
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---
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language:
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- th
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- en
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pipeline_tag: text-generation
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license: llama3
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---
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**Llama-3-Typhoon-1.5X-8B-instruct: Thai Large Language Model (Instruct)**
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**Llama-3-Typhoon-1.5X-8B-instruct** is an 8 billion parameter instruct model designed for Thai 🇹🇭 language. It demonstrates competitive performance with GPT-3.5-turbo, and is optimized for **application** use cases, **Retrieval-Augmented Generation (RAG), constrained generation**, and **reasoning** tasks.
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Built on Typhoon 1.5 8B and Llama 3 8B Instruct. This model is a result of our experiment on **cross-lingual transfer**. It utilizes the [task-arithmetic model editing](https://arxiv.org/abs/2212.04089) technique, combining the Thai understanding capability of Typhoon with the human alignment performance of Llama 3 Instruct.
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Remark: To acknowledge Meta's efforts in creating the foundation model and comply with the license, we explicitly include "llama-3" in the model name.
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## **Model Description**
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- **Model type**: An 8B instruct decoder-only model based on the Llama architecture.
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- **Requirement**: Transformers 4.38.0 or newer.
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- **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧
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- **License**: [**Llama 3 Community License**](https://llama.meta.com/llama3/license/)
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## **Performance**
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We evaluated the model's performance in **Language & Knowledge Capabilities** and **Instruction Following Capabilities**.
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- **Language & Knowledge Capabilities**:
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- Assessed using multiple-choice question-answering datasets such as ThaiExam and MMLU.
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- **Instruction Following Capabilities**:
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- Evaluated based on our beta users' feedback, focusing on two factors:
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- **Human Alignment & Reasoning**: Ability to generate responses that are clear and logically structured across multiple steps.
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- Evaluated using [MT-Bench](https://arxiv.org/abs/2306.05685) — How LLMs can answer embedded knowledge to align with human needs.
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- **Instruction-following**: Ability to adhere to specified constraints in the instruction
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- Evaluated using [IFEval](https://arxiv.org/abs/2311.07911) — How LLMs can follow specified constraints, such as formatting and brevity.
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Remark: We developed the TH pair by translating the original datasets into Thai and conducting a human verification on them.
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### ThaiExam
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| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| Typhoon-1.5 8B | 0.446 | **0.431** | **0.722** | **0.526** | 0.407 | **0.5028** | 0.6136 |
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| Typhoon-1.5X 8B | **0.478** | 0.379 | **0.722** | 0.5 | **0.435** | **0.5028** | 0.6369 |
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| gpt-3.5-turbo-0125 | 0.358 | 0.279 | 0.678 | 0.345 | 0.318 | 0.3956 | **0.700**** |
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** We report the MMLU score that is reported in GPT-4 Tech Report.
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### MT-Bench
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| Model | MT-Bench Thai | MT-Bench English |
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| --- | --- | --- |
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| Typhoon-1.5 8B | 6.402 | 7.275 |
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| Typhoon-1.5X 8B | **6.902** | 7.9 |
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| gpt-3.5-turbo-0125 | 6.186 | **8.181** |
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### IFEval
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| Model | IFEval Thai | IFEval English |
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| --- | --- | --- |
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| Typhoon-1.5 8B | **0.548** | 0.676 |
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| Typhoon-1.5X 8B | **0.548** | **0.691** |
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| gpt-3.5-turbo-0125 | 0.479 | 0.659 |
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## Insight
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We utilized **model editing** techniques and found that the most critical feature for generating accurate Thai answers is located in the backend (the upper layers of the transformer block). Accordingly, we incorporated a high ratio of Typhoon components in these backend layers to enhance our model’s performance.
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## **Usage Example**
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "scb10x/llama-3-typhoon-v1.5x-8b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [...] # add message here
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.4,
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top_p=0.95,
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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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## **Chat Template**
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We use the Llama 3 chat template.
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```python
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{% 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 %}
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```
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## **Intended Uses & Limitations**
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This model is experimental and might not be fully evaluated for all use cases. Developers should assess risks in the context of their specific applications.
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## **Follow us**
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[**https://twitter.com/opentyphoon**](https://twitter.com/opentyphoon)
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## **Support**
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[**https://discord.gg/us5gAYmrxw**](https://discord.gg/us5gAYmrxw)
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## **SCB 10X Typhoon Team**
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- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
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- If you find Typhoon-1.5X useful for your work, please cite it using:
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```
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@article{pipatanakul2023typhoon,
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title={Typhoon: Thai Large Language Models},
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author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
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year={2023},
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journal={arXiv preprint arXiv:2312.13951},
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url={https://arxiv.org/abs/2312.13951}
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}
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```
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## **Contact Us**
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- General & Collaboration: [**kasima@scb10x.com**](mailto:kasima@scb10x.com), [**pathomporn@scb10x.com**](mailto:pathomporn@scb10x.com)
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- Technical: [**kunat@scb10x.com**](mailto:kunat@scb10x.com)
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"_from_model_config": true,
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"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410562
tokenizer.json
Normal file
410562
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user