Instructions to use VTSNLP/Llama3-ViettelSolutions-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VTSNLP/Llama3-ViettelSolutions-8B with Transformers:

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

pipe = pipeline("text-generation", model="VTSNLP/Llama3-ViettelSolutions-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VTSNLP/Llama3-ViettelSolutions-8B")
model = AutoModelForCausalLM.from_pretrained("VTSNLP/Llama3-ViettelSolutions-8B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VTSNLP/Llama3-ViettelSolutions-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VTSNLP/Llama3-ViettelSolutions-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VTSNLP/Llama3-ViettelSolutions-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VTSNLP/Llama3-ViettelSolutions-8B

SGLang

How to use VTSNLP/Llama3-ViettelSolutions-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 "VTSNLP/Llama3-ViettelSolutions-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VTSNLP/Llama3-ViettelSolutions-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "VTSNLP/Llama3-ViettelSolutions-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VTSNLP/Llama3-ViettelSolutions-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VTSNLP/Llama3-ViettelSolutions-8B with Docker Model Runner:
```
docker model run hf.co/VTSNLP/Llama3-ViettelSolutions-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Information

Model Details

Model Description

Llama3-ViettelSolutions-8B is a variant of the Meta Llama-3-8B model, continued pre-trained on the Vietnamese curated dataset and supervised fine-tuned on 5 million samples of Vietnamese instruct data.

Developed by: Viettel Solutions
Funded by: NVIDIA
Model type: Autoregressive transformer model
Language(s) (NLP): Vietnamese, English
License: Llama 3 Community License
Finetuned from model: meta-llama/Meta-Llama-3-8B

Uses

Example snippet for usage with Transformers:

import transformers
import torch

model_id = "VTSNLP/Llama3-ViettelSolutions-8B"

pipeline = transformers.pipeline(
    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
pipeline("Xin chào!")

Training Details

Training Data

Dataset for continue pretrain: Vietnamese curated dataset
Dataset for supervised fine-tuning: Instruct general dataset

Training Procedure

Preprocessing

[More Information Needed]

Training Hyperparameters

Training regime: bf16 mixed precision
Data sequence length: 8192
Tensor model parallel size: 4
Pipelinemodel parallel size: 1
Context parallel size: 1
Micro batch size: 1
Global batch size: 512

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

[More Information Needed]

Technical Specifications

Compute Infrastructure: NVIDIA DGX
Hardware: 4 x A100 80GB
Software: NeMo Framework

Citation

BibTeX:

[More Information Needed]

APA:

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More Information

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Model Card Authors

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Model Card Contact

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Safetensors

Model size

8B params

Tensor type

F32

Model tree for VTSNLP/Llama3-ViettelSolutions-8B

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

(591)

this model

Quantizations

2 models

VTSNLP
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Llama3-ViettelSolutions-8B

Model Information

Model Details

Model Description

Uses

Training Details

Training Data

Training Procedure

Preprocessing

Training Hyperparameters

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary

Technical Specifications

Citation

More Information

Model Card Authors

Model Card Contact

Model tree for VTSNLP/Llama3-ViettelSolutions-8B

Dataset used to train VTSNLP/Llama3-ViettelSolutions-8B