Instructions to use nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B")
model = AutoModelForCausalLM.from_pretrained("nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B

SGLang

How to use nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B with Docker Model Runner:
```
docker model run hf.co/nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B
```

jackJessada Pakawat Phasook commited on Oct 24, 2024

Commit

78e9114

verified ·

1 Parent(s): 701e828

update readme.md (#2)

Browse files

- update readme.md (a4de955b458a2110d5ea06509a1b12db0dccdf5c)

Co-authored-by: Pakawat Phasook <SuperkingbasSKB@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -16,17 +16,17 @@ tags:
 - code
 - legal
 ---
-# OpenThaiLLM-DoodNiLT-Instruct: Thai & China Large Language Model (Instruct)
-**OpenThaiLLM-DoodNiLT-Instruct** is an 7 billion parameter instruct model designed for Thai 🇹🇭 & China 🇨🇳 language.
-It demonstrates competitive performance with GPT-3.5-turbo and llama-3-typhoon-v1.5-8b-instruct, and is optimized for application use cases, Retrieval-Augmented Generation (RAG),
-constrained generation, and reasoning tasks.is a Thai 🇹🇭 & China 🇨🇳 large language model with 7 billion parameters, and it is based on Qwen2-7B.
 ## Introduction
-Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model.
 Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
-Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
 <br>
@@ -81,9 +81,9 @@ print(response)
 ```
 ## Evaluation Performance Few-shot (5 shot)
-| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam (1 shot) | MMLU |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| DoodNiLT-7B | **0.5185** | **0.6421** | **0.6461** | **0.4224** | **0.3937** | **0.5245** | **0.5355** | 0.6644 |
 | llama-3-typhoon-v1.5-8b | 0.3765 | 0.3473 | 0.5538 | 0.4137 | 0.2913 | 0.3965 | 0.4312 | 0.6451 |
 | OpenThaiGPT-1.0.0-7B | 0.3086 | 0.3052 | 0.4153 | 0.3017 | 0.2755 | 0.3213 | 0.255 | 0.3512 |
 | Meta-Llama-3.1-8B | 0.3641 | 0.2631 | 0.2769 | 0.3793 | 0.1811 | 0.2929 | 0.4239 | 0.6591 |

 - code
 - legal
 ---
+# OpenThaiLLM-Prebuilt: Thai & China & English Large Language Model
+**OpenThaiLLM-Prebuilt** is an 7 billion parameter instruct model designed for Thai 🇹🇭 & China 🇨🇳 language.
+It demonstrates an amazing result, and is optimized for application use cases, Retrieval-Augmented Generation (RAG), Web deployment
+constrained generation, and reasoning tasks.is a Thai 🇹🇭 & China 🇨🇳 large language model with 7 billion parameters, and it is based on Qwen2.5-7B.
 ## Introduction
+Qwen2.5 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model.
 Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
+Qwen2.5-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
 <br>
 ```
 ## Evaluation Performance Few-shot (5 shot)
+| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average ThaiExam) | MMLU | M3Exam (1 shot) | M6Exam(5shot) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| OpenthaiLLM-Prebuilt-7B | **0.5493** | **0.6315** | **0.6307** | **0.4655** | **0.37** | **0.5294** | **0.7054** | **0.5705** | **0.596** |
 | llama-3-typhoon-v1.5-8b | 0.3765 | 0.3473 | 0.5538 | 0.4137 | 0.2913 | 0.3965 | 0.4312 | 0.6451 |
 | OpenThaiGPT-1.0.0-7B | 0.3086 | 0.3052 | 0.4153 | 0.3017 | 0.2755 | 0.3213 | 0.255 | 0.3512 |
 | Meta-Llama-3.1-8B | 0.3641 | 0.2631 | 0.2769 | 0.3793 | 0.1811 | 0.2929 | 0.4239 | 0.6591 |