Text Generation
Transformers
Safetensors
English
Persian
llama
LLM
llama-3
PartAI
conversational
text-generation-inference
Instructions to use PartAI/Dorna-Llama3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PartAI/Dorna-Llama3-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PartAI/Dorna-Llama3-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PartAI/Dorna-Llama3-8B-Instruct") model = AutoModelForCausalLM.from_pretrained("PartAI/Dorna-Llama3-8B-Instruct") 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 PartAI/Dorna-Llama3-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PartAI/Dorna-Llama3-8B-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": "PartAI/Dorna-Llama3-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PartAI/Dorna-Llama3-8B-Instruct
- SGLang
How to use PartAI/Dorna-Llama3-8B-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 "PartAI/Dorna-Llama3-8B-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": "PartAI/Dorna-Llama3-8B-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 "PartAI/Dorna-Llama3-8B-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": "PartAI/Dorna-Llama3-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PartAI/Dorna-Llama3-8B-Instruct with Docker Model Runner:
docker model run hf.co/PartAI/Dorna-Llama3-8B-Instruct
دیتاست
#1
by makeAmericaGreatAgain - opened
سلام
ممکنه بگید این مدل روی چه مجموعه داده ای ترین شده؟
سلام!
دیتاستهای مورد استفاده در این مدل شامل متون خام و همچنین دادههای دستورالعملی هستند که با دقت و مهارت توسط نیروی انسانی و تکنیکهای خودکار تهیه شدهاند.
از اینکه از مدل ما استفاده کردید، سپاسگزاریم و مشتاقانه منتظر بازخورد شما در مورد تجربه استفاده از آن هستیم.
How many tokens used for training or fine-tuning?
How many tokens used for training or fine-tuning?\
در حال حاضر نمیتوانیم جزئیات بیشتری ارائه دهیم. اما نوید میدهم که در آینده نزدیک، بخشی از دیتاست در اختیار همگان قرار خواهد گرفت.
با ما همراه باشید!
MiladMola changed discussion status to closed