Instructions to use LLaMAX/LLaMAX3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLaMAX/LLaMAX3-8B with Transformers:

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

pipe = pipeline("text-generation", model="LLaMAX/LLaMAX3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLaMAX/LLaMAX3-8B")
model = AutoModelForCausalLM.from_pretrained("LLaMAX/LLaMAX3-8B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLaMAX/LLaMAX3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLaMAX/LLaMAX3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLaMAX/LLaMAX3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLaMAX/LLaMAX3-8B

SGLang

How to use LLaMAX/LLaMAX3-8B 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 "LLaMAX/LLaMAX3-8B" \
    --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": "LLaMAX/LLaMAX3-8B",
		"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 "LLaMAX/LLaMAX3-8B" \
        --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": "LLaMAX/LLaMAX3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLaMAX/LLaMAX3-8B with Docker Model Runner:
```
docker model run hf.co/LLaMAX/LLaMAX3-8B
```

huangtao6 commited on Jul 9, 2024

Commit

d8949ca

1 Parent(s): 739b1b5

update readme

Browse files

Files changed (1) hide show

README.md +4 -35

README.md CHANGED Viewed

@@ -6,45 +6,14 @@
 - **Repository**: https://github.com/CONE-MT/LLaMAX/
 ### Model Description
-LLaMAX is a multilingual language model, developed through continued pre-training on Llama3, and supports over 100 languages.
-Its translation capabilities far exceed general models of the same scale, and it can serve as a base model to support downstream multilingual tasks.
-### 🔥 Effortless Multilingual Translation with a Simple Prompt
-LLaMAX supports translation between more than 100 languages, surpassing the performance of similarly scaled LLMs.
-```angular2html
-def Prompt_template(query, src_language, trg_language):
-    instruction = f'Translate the following sentences from {src_language} to {trg_language}.'
-    prompt = (
-        'Below is an instruction that describes a task, paired with an input that provides further context. '
-        'Write a response that appropriately completes the request.\n'
-        f'### Instruction:\n{instruction}\n'
-        f'### Input:\n{query}\n### Response:'
-    )
-    return prompt
-```
-And then run the following codes to execute translation:
-```angular2html
-from transformers import AutoTokenizer, LlamaForCausalLM
-model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-query = "你好，今天是个好日子"
-prompt = Prompt_template(query, 'Chinese', 'English')
-inputs = tokenizer(prompt, return_tensors="pt")
-generate_ids = model.generate(inputs.input_ids, max_length=30)
-tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-# => "Hello, today is a good day"
-```
 ### 🔥 Effective Base Model for Multilingual Task
-LLaMAX preserves its efficacy in general tasks and improves the performance on multilingual tasks.
 We fine-tuned LLaMAX using only the English training set of downstream task, which also shows significant improvements in non-English. We provide fine-tuning LLaMAX models for the following three tasks:
 - **Math Reasoning**: https://huggingface.co/LLaMAX/LLaMAX2-7B-MetaMath

 - **Repository**: https://github.com/CONE-MT/LLaMAX/
 ### Model Description
+LLaMAX3-8B is a multilingual language base model, developed through continued pre-training on Llama3, and supports over 100 languages.
+LLaMAX3-8B can serve as a base model to support downstream multilingual tasks but without instruct-following capability.
+We further fine-tuned LLaMAX2-7B on Alpaca dataset to enhance its instruct-following capabilities. The model is available at https://huggingface.co/LLaMAX/LLaMAX3-8B-Alpaca.
 ### 🔥 Effective Base Model for Multilingual Task
+LLaMAX2-7B preserves its efficacy in general tasks and improves the performance on multilingual tasks.
 We fine-tuned LLaMAX using only the English training set of downstream task, which also shows significant improvements in non-English. We provide fine-tuning LLaMAX models for the following three tasks:
 - **Math Reasoning**: https://huggingface.co/LLaMAX/LLaMAX2-7B-MetaMath