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

18b5bb2

1 Parent(s): a8bdb1e

update readme

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README.md +7 -11

README.md CHANGED Viewed

@@ -1,11 +1,9 @@
 ### Model Sources
-Paper: LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
-Link: https://arxiv.org/pdf/2407
-Repository: https://github.com/CONE-MT/
 ### Model Description
 LLaMAX is a multilingual language model, developed through continued pre-training on Llama3, and supports over 100 languages.
@@ -17,7 +15,7 @@ Its translation capabilities far exceed general models of the same scale, and it
 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. '
@@ -49,11 +47,9 @@ tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
 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
-Commonsense Reasoning: https://huggingface.co/LLaMAX/LLaMAX2-7B-X-CSQA
-Natural Language Inference: https://huggingface.co/LLaMAX/LLaMAX2-7B-XNLI
 ### Supported Languages

 ### Model Sources
+- **Paper**: LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
+- **Link**:
+- **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.
 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. '
 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
+- **Commonsense Reasoning**: https://huggingface.co/LLaMAX/LLaMAX2-7B-X-CSQA
+- **Natural Language Inference**: https://huggingface.co/LLaMAX/LLaMAX2-7B-XNLI
 ### Supported Languages