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README.md

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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-v1.5x-70b-instruct - GGUF
+- Model creator: https://huggingface.co/scb10x/
+- Original model: https://huggingface.co/scb10x/llama-3-typhoon-v1.5x-70b-instruct/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q2_K.gguf) | Q2_K | 24.56GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.IQ3_S.gguf) | IQ3_S | 28.79GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.IQ3_M.gguf) | IQ3_M | 29.74GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q3_K.gguf) | Q3_K | 31.91GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/blob/main/llama-3-typhoon-v1.5x-70b-instruct.Q4_0.gguf) | Q4_0 | 37.22GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | IQ4_NL | 37.58GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q4_K_S | 37.58GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q4_K | 39.6GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q4_K_M | 39.6GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q4_1 | 41.27GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q5_0 | 45.32GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q5_K_S | 45.32GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q5_K | 46.52GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q5_K_M | 46.52GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q5_1 | 49.36GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q6_K | 53.91GB |
+| [llama-3-typhoon-v1.5x-70b-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/scb10x_-_llama-3-typhoon-v1.5x-70b-instruct-gguf/tree/main/) | Q8_0 | 69.83GB |
+
+
+
+
+Original model description:
+---
+language:
+- th
+- en
+pipeline_tag: text-generation
+license: llama3
+---
+**Llama-3-Typhoon-1.5X-70B-instruct: Thai Large Language Model (Instruct)**
+
+**Llama-3-Typhoon-1.5X-70B-instruct** is a 70 billion parameter instruct model designed for Thai 🇹🇭 language. It demonstrates competitive performance with GPT-4-0612, and is optimized for **application** use cases, **Retrieval-Augmented Generation (RAG), constrained generation**, and **reasoning** tasks.
+
+Built on Typhoon 1.5 70B (not yet released) and Llama 3 70B 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.
+
+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.
+
+## **Model Description**
+
+- **Model type**: A 70B instruct decoder-only model based on the 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**
+
+We evaluated the model's performance in **Language & Knowledge Capabilities** and **Instruction Following Capabilities**.
+
+- **Language & Knowledge Capabilities**:
+    - Assessed using multiple-choice question-answering datasets such as ThaiExam and MMLU.
+- **Instruction Following Capabilities**:
+    - Evaluated based on beta users' feedback, focusing on two factors:
+        - **Human Alignment & Reasoning**: Ability to generate responses that are clear and logically structured across multiple steps.
+            - Evaluated using [MT-Bench](https://arxiv.org/abs/2306.05685) — How LLMs can align with human needs.
+        - **Instruction-following**: Ability to adhere to specified constraints in the instructions.
+            - Evaluated using [IFEval](https://arxiv.org/abs/2311.07911) — How LLMs can follow specified constraints, such as formatting and brevity.
+- **Agentic Capabilities**:
+    - Evaluated in agent use-cases using [Hugging Face's Transformer Agents](https://huggingface.co/blog/agents) and the associated [benchmark](https://huggingface.co/blog/open-source-llms-as-agents).
+
+Remark: We developed the Thai (TH) pairs by translating the original datasets into Thai through machine and human methods.
+
+### ThaiExam
+
+| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Typhoon-1.5X 70B | **0.565** | 0.68 | **0.778** | **0.517** | 0.56 | **0.620** | 0.7945 |
+| gpt-4-0612 | 0.493 | **0.69** | 0.744 | 0.509 | **0.616** | 0.610 | **0.864**** |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| gpt-4o | 0.62 | 0.63 | 0.789 | 0.56 | 0.623 | 0.644 | 0.887** |
+
+** We report the MMLU score that is reported in [GPT-4o Tech Report](https://openai.com/index/hello-gpt-4o/). 
+
+### MT-Bench
+
+| Model | MT-Bench Thai | MT-Bench English |
+| --- | --- | --- |
+| Typhoon-1.5X 70B | **8.029** | **8.797** |
+| gpt-4-0612 | 7.801 | 8.671 |
+| --- | --- | --- |
+| gpt-4o | 8.514 | 9.184 |
+
+### IFEval
+
+| Model | IFEval Thai | IFEval English |
+| --- | --- | --- |
+| Typhoon-1.5X 70B | **0.645** | **0.810** |
+| gpt-4-0612 | 0.612 | 0.793* |
+| --- | --- | --- |
+| gpt-4o | 0.737 | 0.871 |
+
+* We report the number from IFEval paper.
+
+### Agent
+
+| Model | GAIA - Thai/English | GSM8K - Thai/English | HotpotQA - Thai/English |
+| --- | --- | --- | --- |
+| gpt-3.5-turbo-0125 | **18.42**/37.5 | 70/80 | 39.56/59 |
+| Typhoon-1.5X 70B | 17.10/36.25 | 80/95 | 52.7/65.83 |
+| gpt-4-0612 | 17.10/**38.75** | **90**/**100** | **56.41**/**76.25** |
+| --- | --- | --- | --- |
+| gpt-4o | 44.73/57.5 | 100/100 | 71.64/76.58 |
+
+## Insight
+
+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.
+
+## **Usage Example**
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "scb10x/llama-3-typhoon-v1.5x-70b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+) # We don't recommend using BNB 4-bit (load_in_4bit) here. Instead, use AWQ, as detailed here: https://huggingface.co/scb10x/llama-3-typhoon-v1.5x-70b-instruct-awq.
+
+messages = [...] # add message here
+
+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=0.95,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
+
+## **Chat Template**
+
+We use the Llama 3 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 experimental and might not be fully evaluated for all use cases. Developers should assess risks in the context of their specific applications.
+
+## **Follow us**
+
+[**https://twitter.com/opentyphoon**](https://twitter.com/opentyphoon)
+
+## **Support**
+
+[**https://discord.gg/CqyBscMFpg**](https://discord.gg/CqyBscMFpg)
+
+## **SCB 10X Typhoon Team**
+
+- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
+- If you find Typhoon-1.5X 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)
+