Instructions to use pmahdavi/Llama-3.1-8B-general with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pmahdavi/Llama-3.1-8B-general with Transformers:

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

pipe = pipeline("text-generation", model="pmahdavi/Llama-3.1-8B-general")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pmahdavi/Llama-3.1-8B-general")
model = AutoModelForCausalLM.from_pretrained("pmahdavi/Llama-3.1-8B-general", device_map="auto")
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 pmahdavi/Llama-3.1-8B-general with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pmahdavi/Llama-3.1-8B-general"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pmahdavi/Llama-3.1-8B-general",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pmahdavi/Llama-3.1-8B-general

SGLang

How to use pmahdavi/Llama-3.1-8B-general 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 "pmahdavi/Llama-3.1-8B-general" \
    --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": "pmahdavi/Llama-3.1-8B-general",
		"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 "pmahdavi/Llama-3.1-8B-general" \
        --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": "pmahdavi/Llama-3.1-8B-general",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pmahdavi/Llama-3.1-8B-general with Docker Model Runner:
```
docker model run hf.co/pmahdavi/Llama-3.1-8B-general
```

Llama-3.1-8B General Model

This is a fine-tuned Llama-3.1-8B model specialized for general instruction following tasks. This checkpoint was released alongside https://arxiv.org/abs/2509.11167.

Model Details

Base model: Llama-3.1-8B
Training dataset: tulu3_mixture_general
Learning rate: 5e-06
Effective batch size: 128

Export Files

This repository includes export files for state averaging and other advanced techniques.

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for pmahdavi/Llama-3.1-8B-general

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1473)

this model

Space using pmahdavi/Llama-3.1-8B-general 1

Paper for pmahdavi/Llama-3.1-8B-general

Harnessing Optimization Dynamics for Curvature-Informed Model Merging

Paper • 2509.11167 • Published Sep 14, 2025 • 1

Evaluation results

Training Loss on tulu3_mixture_general
self-reported

1.030