Instructions to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="lianghsun/Llama-3.2-Taiwan-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lianghsun/Llama-3.2-Taiwan-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("lianghsun/Llama-3.2-Taiwan-3B-Instruct", 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 lianghsun/Llama-3.2-Taiwan-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lianghsun/Llama-3.2-Taiwan-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct

SGLang

How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct 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 "lianghsun/Llama-3.2-Taiwan-3B-Instruct" \
    --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": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"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 "lianghsun/Llama-3.2-Taiwan-3B-Instruct" \
        --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": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct
```

lianghsun commited on Jan 1, 2025

Commit

44cd75b

1 Parent(s): 89cf561

Fine-tuning is based on the foundation model version v2024.12.28, and it uses self-prepared instruction datasets for this round of fine-tuning.

Browse files

Files changed (3) hide show

README.md +5 -13
model-00001-of-00002.safetensors +1 -1
model-00002-of-00002.safetensors +2 -2

README.md CHANGED Viewed

@@ -24,12 +24,9 @@ datasets:
 - lianghsun/tw-contract-review-chat
 - lianghsun/reasoning-base-20k-chat
 - lianghsun/vulnerability-mitigation-qa-zh_tw
-- benchang1110/Belle-Taide
 - rombodawg/Everything_Instruct_Multilingual
-- BAAI/Infinity-Instruct
-- nisaar/LLAMA2_Legal_Dataset_4.4k_Instructions
 - xzuyn/manythings-translations-alpaca
-- neural-bridge/rag-hallucination-dataset-1000
 - neural-bridge/rag-dataset-12000
 - minyichen/glaive_toolcall_zh_tw
 pipeline_tag: text-generation
@@ -999,6 +996,7 @@ metrics:
   | Update Date  | Model Version         | Key Changes                         |
   |--------------|-----------------------|-------------------------------------|
   | 2024/11/27   | v2024.11.27           | Completed SFT training (5/5 epochs). Preparing for multi-round DPO training. |
   | 2024/11/25   | v2024.11.25           | Updated model version to v2024.11.25, training progressed to (3/5) epochs. Still in SFT stage, DPO training remains pending. |
   | 2024/11/22   | v2024.11.22           | Initial upload: Model version v2024.11.22, training completed up to (1/5) epochs. Currently trained only on SFT, DPO training not yet performed. |
@@ -1096,7 +1094,6 @@ docker run --runtime nvidia --gpus all \
   - [lianghsun/tw-law-article-qa](https://huggingface.co/datasets/lianghsun/tw-law-article-qa)
   - [lianghsun/tw-judgment-qa](https://huggingface.co/datasets/lianghsun/tw-judgment-qa)
   - [lianghsun/tw-bar-examination-2020-chat](https://huggingface.co/datasets/lianghsun/tw-bar-examination-2020-chat)
-  - [lianghsun/tw-emergency-medicine-bench](https://huggingface.co/datasets/lianghsun/tw-emergency-medicine-bench)
   - [lianghsun/tw-structured-law-article](https://huggingface.co/datasets/lianghsun/tw-structured-law-article)
   - [lianghsun/tw-judgment-gist-chat](https://huggingface.co/datasets/lianghsun/tw-judgment-gist-chat)
   - [lianghsun/vulnerability-mitigation-qa-zh_tw](https://huggingface.co/datasets/lianghsun/vulnerability-mitigation-qa-zh_tw)
@@ -1104,7 +1101,6 @@ docker run --runtime nvidia --gpus all \
   - [lianghsun/reasoning-base-20k-chat](https://huggingface.co/datasets/lianghsun/reasoning-base-20k-chat)
   - [lianghsun/tw-contract-review-chat](https://huggingface.co/datasets/lianghsun/tw-contract-review-chat)
   - [lianghsun/tw-legal-methodology-chat](https://huggingface.co/datasets/lianghsun/tw-legal-methodology-chat)
-  - [benchang1110/Belle-Taide](https://huggingface.co/datasets/benchang1110/Belle-Taide)
   - [minyichen/glaive_toolcall_zh_tw](https://huggingface.co/datasets/minyichen/glaive_toolcall_zh_tw)
 </details>
@@ -1113,11 +1109,8 @@ docker run --runtime nvidia --gpus all \
   <summary><b>多國語系對話資料集</b></summary>
   - [rombodawg/Everything_Instruct_Multilingual](https://huggingface.co/datasets/rombodawg/Everything_Instruct_Multilingual)
-  - [BAAI/Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct)
-  - [nisaar/LLAMA2_Legal_Dataset_4.4k_Instructions](https://huggingface.co/datasets/nisaar/LLAMA2_Legal_Dataset_4.4k_Instructions)
   - [xzuyn/manythings-translations-alpaca](https://huggingface.co/datasets/xzuyn/manythings-translations-alpaca)
   - [neural-bridge/rag-dataset-12000](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000)
-  - [neural-bridge/rag-hallucination-dataset-1000](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000)
 </details>
@@ -1125,10 +1118,9 @@ docker run --runtime nvidia --gpus all \
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
@@ -1416,4 +1408,4 @@ base_model: lianghsun/Llama-3.2-Taiwan-3B-Instruct
 - Transformers 4.45.2
 - Pytorch 2.4.1+cu121
 - Datasets 2.21.0
-- Tokenizers 0.20.0

 - lianghsun/tw-contract-review-chat
 - lianghsun/reasoning-base-20k-chat
 - lianghsun/vulnerability-mitigation-qa-zh_tw
+- lianghsun/tw-instruct
 - rombodawg/Everything_Instruct_Multilingual
 - xzuyn/manythings-translations-alpaca
 - neural-bridge/rag-dataset-12000
 - minyichen/glaive_toolcall_zh_tw
 pipeline_tag: text-generation
   | Update Date  | Model Version         | Key Changes                         |
   |--------------|-----------------------|-------------------------------------|
+  | 2025/01/01   | v2025.01.01           | Fine-tuning is based on the [foundation model](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) version v2024.12.28, and it uses self-prepared instruction datasets for this round of fine-tuning. |
   | 2024/11/27   | v2024.11.27           | Completed SFT training (5/5 epochs). Preparing for multi-round DPO training. |
   | 2024/11/25   | v2024.11.25           | Updated model version to v2024.11.25, training progressed to (3/5) epochs. Still in SFT stage, DPO training remains pending. |
   | 2024/11/22   | v2024.11.22           | Initial upload: Model version v2024.11.22, training completed up to (1/5) epochs. Currently trained only on SFT, DPO training not yet performed. |
   - [lianghsun/tw-law-article-qa](https://huggingface.co/datasets/lianghsun/tw-law-article-qa)
   - [lianghsun/tw-judgment-qa](https://huggingface.co/datasets/lianghsun/tw-judgment-qa)
   - [lianghsun/tw-bar-examination-2020-chat](https://huggingface.co/datasets/lianghsun/tw-bar-examination-2020-chat)
   - [lianghsun/tw-structured-law-article](https://huggingface.co/datasets/lianghsun/tw-structured-law-article)
   - [lianghsun/tw-judgment-gist-chat](https://huggingface.co/datasets/lianghsun/tw-judgment-gist-chat)
   - [lianghsun/vulnerability-mitigation-qa-zh_tw](https://huggingface.co/datasets/lianghsun/vulnerability-mitigation-qa-zh_tw)
   - [lianghsun/reasoning-base-20k-chat](https://huggingface.co/datasets/lianghsun/reasoning-base-20k-chat)
   - [lianghsun/tw-contract-review-chat](https://huggingface.co/datasets/lianghsun/tw-contract-review-chat)
   - [lianghsun/tw-legal-methodology-chat](https://huggingface.co/datasets/lianghsun/tw-legal-methodology-chat)
   - [minyichen/glaive_toolcall_zh_tw](https://huggingface.co/datasets/minyichen/glaive_toolcall_zh_tw)
 </details>
   <summary><b>多國語系對話資料集</b></summary>
   - [rombodawg/Everything_Instruct_Multilingual](https://huggingface.co/datasets/rombodawg/Everything_Instruct_Multilingual)
   - [xzuyn/manythings-translations-alpaca](https://huggingface.co/datasets/xzuyn/manythings-translations-alpaca)
   - [neural-bridge/rag-dataset-12000](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000)
 </details>
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing
+(WIP)
 #### Training Hyperparameters
 - Transformers 4.45.2
 - Pytorch 2.4.1+cu121
 - Datasets 2.21.0
+- Tokenizers 0.20.0

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