Instructions to use PartAI/Dorna2-Llama3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PartAI/Dorna2-Llama3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="PartAI/Dorna2-Llama3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PartAI/Dorna2-Llama3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("PartAI/Dorna2-Llama3.1-8B-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

vLLM

How to use PartAI/Dorna2-Llama3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PartAI/Dorna2-Llama3.1-8B-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/Dorna2-Llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PartAI/Dorna2-Llama3.1-8B-Instruct

SGLang

How to use PartAI/Dorna2-Llama3.1-8B-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/Dorna2-Llama3.1-8B-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/Dorna2-Llama3.1-8B-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/Dorna2-Llama3.1-8B-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/Dorna2-Llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PartAI/Dorna2-Llama3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/PartAI/Dorna2-Llama3.1-8B-Instruct
```

bug in fine tuning dorna 2 using unsloth library

by intai2070 - opened Dec 19, 2025

Discussion

intai2070

Dec 19, 2025

Unsloth: The tokenizer PartAI/Dorna2-Llama3.1-8B-Instruct
does not have a {% if add_generation_prompt %} for generation purposes.
Please file a bug report to the maintainers of PartAI/Dorna2-Llama3.1-8B-Instruct - thanks!

intai2070

Dec 19, 2025

•

edited Dec 19, 2025

How to fix (google ai mode answer):

Correct the Chat Template: Manually edit the tokenizer_config.json file in your exported or loaded model.
Find the chat_template field.
It should look something like: {{...}} {% if add_generation_prompt %}{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }{% endif %} (The exact content varies by model, but the {% if add_generation_prompt %} part is key).

intai2070

Dec 19, 2025

but your chat template is:

"chat_template": "{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>\n\n' + system_message + '<|eot_id|>' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\n\n' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}"

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