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

Libraries

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
```

ModelFile for Ollama

by Zizigolo - opened Jun 10, 2024

Discussion

Zizigolo

Jun 10, 2024

سلام
من میخواستم ببینم اگه بخوام این مدل رو توی
Ollama
وارد کنم نیاز به یک
ModelFile
داره. آیا مدل فایل درنا با مدل فایل لاما3 8 یکسانه؟

MiladMola

Part DP AI org Jun 10, 2024

سلام عزیز!

برای سرو کردن مدل با Ollama، باید مراحل زیر را طی کنید:

با استفاده از llama.cpp، مدل را به فرمت GGUF تبدیل کنید. در صورت تمایل، می‌توانید انواع کوانتیزیشن را بر اساس نیازتان اعمال کنید.
پس از تبدیل، ModelFile زیر را بسازید.

FROM /path/to/converted-model.gguf

TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

SYSTEM """You are a helpful assistant."""

3. با دو دستور زیر می‌توانید از مدل با Ollama استفاده کنید.

ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>
ollama run choose-a-model-name

برای توضیحات کامل سند را مطالعه کنید.

Zizigolo

Jun 11, 2024

عالی ممنون

Zizigolo changed discussion status to closed Jun 11, 2024

Zizigolo changed discussion status to open Jun 11, 2024

MiladMola changed discussion status to closed Jun 11, 2024

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