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

Libraries

How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/LLaMAX3-8B-Alpaca-GGUF",
	filename="LLaMAX3-8B-Alpaca.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Ollama
How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/LLaMAX3-8B-Alpaca-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/LLaMAX3-8B-Alpaca-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/LLaMAX3-8B-Alpaca-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/LLaMAX3-8B-Alpaca-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/LLaMAX3-8B-Alpaca-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LLaMAX3-8B-Alpaca-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/LLaMAX3-8B-Alpaca-GGUF

This is quantized version of LLaMAX/LLaMAX3-8B-Alpaca created using llama.cpp

Model Description

Model Sources

Paper: LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
Link: https://arxiv.org/pdf/2407.05975
Repository: https://github.com/CONE-MT/LLaMAX/

Model Description

LLaMAX is a language model with powerful multilingual capabilities without loss instruction-following capabilities.

We collected extensive training sets in 102 languages for continued pre-training of Llama2 and leveraged the English instruction fine-tuning dataset, Alpaca, to fine-tune its instruction-following capabilities.

🔥 Effortless Multilingual Translation with a Simple Prompt

LLaMAX supports translation between more than 100 languages, surpassing the performance of similarly scaled LLMs.

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:

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"

🔥 Excellent Translation Performance

LLaMAX3-8B-Alpaca achieves an average spBLEU score improvement of over 5 points compared to the LLaMA3-8B-Alpaca model on the Flores-101 dataset.

System	Size	en-X (COMET)	en-X (BLEU)	zh-X (COMET)	zh-X (BLEU)	de-X (COMET)	de-X (BLEU)	ne-X (COMET)	ne-X (BLEU)	ar-X (COMET)	ar-X (BLEU)	az-X (COMET)	az-X (BLEU)	ceb-X (COMET)	ceb-X (BLEU)
LLaMA3-8B-Alpaca	8B	67.97	17.23	64.65	10.14	64.67	13.62	62.95	7.96	63.45	11.27	60.61	6.98	55.26	8.52
LLaMAX3-8B-Alpaca	8B	75.52	22.77	73.16	14.43	73.47	18.95	75.13	15.32	72.29	16.42	72.06	12.41	68.88	15.85

System	Size	X-en (COMET)	X-en (BLEU)	X-zh (COMET)	X-zh (BLEU)	X-de (COMET)	X-de (BLEU)	X-ne (COMET)	X-ne (BLEU)	X-ar (COMET)	X-ar (BLEU)	X-az (COMET)	X-az (BLEU)	X-ceb (COMET)	X-ceb (BLEU)
LLaMA3-8B-Alpaca	8B	77.43	26.55	73.56	13.17	71.59	16.82	46.56	3.83	66.49	10.20	58.30	4.81	52.68	4.18
LLaMAX3-8B-Alpaca	8B	81.28	31.85	78.34	16.46	76.23	20.64	65.83	14.16	75.84	15.45	70.61	9.32	63.35	12.66

Supported Languages

Akrikaans (af), Amharic (am), Arabic (ar), Armenian (hy), Assamese (as), Asturian (ast), Azerbaijani (az), Belarusian (be), Bengali (bn), Bosnian (bs), Bulgarian (bg), Burmese (my), Catalan (ca), Cebuano (ceb), Chinese Simpl (zho), Chinese Trad (zho), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Filipino (tl), Finnish (fi), French (fr), Fulah (ff), Galician (gl), Ganda (lg), Georgian (ka), German (de), Greek (el), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Igbo (ig), Indonesian (id), Irish (ga), Italian (it), Japanese (ja), Javanese (jv), Kabuverdianu (kea), Kamba (kam), Kannada (kn), Kazakh (kk), Khmer (km), Korean (ko), Kyrgyz (ky), Lao (lo), Latvian (lv), Lingala (ln), Lithuanian (lt), Luo (luo), Luxembourgish (lb), Macedonian (mk), Malay (ms), Malayalam (ml), Maltese (mt), Maori (mi), Marathi (mr), Mongolian (mn), Nepali (ne), Northern Sotho (ns), Norwegian (no), Nyanja (ny), Occitan (oc), Oriya (or), Oromo (om), Pashto (ps), Persian (fa), Polish (pl), Portuguese (pt), Punjabi (pa), Romanian (ro), Russian (ru), Serbian (sr), Shona (sn), Sindhi (sd), Slovak (sk), Slovenian (sl), Somali (so), Sorani Kurdish (ku), Spanish (es), Swahili (sw), Swedish (sv), Tajik (tg), Tamil (ta), Telugu (te), Thai (th), Turkish (tr), Ukrainian (uk), Umbundu (umb), Urdu (ur), Uzbek (uz), Vietnamese (vi), Welsh (cy), Wolof (wo), Xhosa (xh), Yoruba (yo), Zulu (zu)

Model Index

We implement multiple versions of the LLaMAX model, the model links are as follows:

Model	LLaMAX	LLaMAX-Alpaca
Llama-2	Link	Link
Llama-3	Link	Link

Model Citation

If our model helps your work, please cite this paper:

@misc{lu2024llamaxscalinglinguistichorizons,
      title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages}, 
      author={Yinquan Lu and Wenhao Zhu and Lei Li and Yu Qiao and Fei Yuan},
      year={2024},
      eprint={2407.05975},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.05975}, 
}

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

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6-bit

8-bit

Model tree for QuantFactory/LLaMAX3-8B-Alpaca-GGUF

Base model

LLaMAX/LLaMAX3-8B-Alpaca

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this model

Paper for QuantFactory/LLaMAX3-8B-Alpaca-GGUF

LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages

Paper • 2407.05975 • Published Jul 8, 2024 • 36