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

Libraries

How to use pearl-ai/Llama-3.1-8B-Instruct-pearl with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pearl-ai/Llama-3.1-8B-Instruct-pearl")
model = AutoModelForCausalLM.from_pretrained("pearl-ai/Llama-3.1-8B-Instruct-pearl", 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 pearl-ai/Llama-3.1-8B-Instruct-pearl with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pearl-ai/Llama-3.1-8B-Instruct-pearl

SGLang

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

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

Llama-3.1-8B-Instruct-pearl / README.md

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	---
	language:
	- en
	license: llama3.1
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- pearl
	- llama
	- llama-3.1
	- instruct
	- text-generation
	- vllm
	- mining
	base_model: meta-llama/Llama-3.1-8B-Instruct
	---

	# pearl-ai/Llama-3.1-8B-Instruct-pearl

	Pearl-certified variant of Llama-3.1-8B-Instruct, intended to run with the Pearl vLLM mining plugin.

	- Project website: [https://pearlresearch.ai](https://pearlresearch.ai)
	- Pearl repository: [https://github.com/pearl-research-labs/pearl](https://github.com/pearl-research-labs/pearl)
	- Miner docs: [https://github.com/pearl-research-labs/pearl/tree/master/miner](https://github.com/pearl-research-labs/pearl/tree/master/miner)

	## Model Details

	- Base model: `meta-llama/Llama-3.1-8B-Instruct`
	- Model type: Causal LLM (instruction-tuned)
	- Primary runtime target: Pearl vLLM plugin (`miner/vllm-miner`)
	- Intended use: Text generation with Pearl mining integration

	## Intended Use

	This model is intended to be served through the Pearl miner stack, where vLLM inference is integrated with Pearl mining workflows.

	Typical flow:
	1. Run `pearld` with RPC enabled.
	2. Start the Pearl miner/vLLM stack.
	3. Serve this model through vLLM while Pearl gateway/miner components handle mining-side integration.

	## How To Use (Pearl vLLM Plugin)

	Follow the miner setup from the Pearl repo:
	- [pearl/miner README](https://github.com/pearl-research-labs/pearl/tree/master/miner)

	High-level prerequisites:
	- Python 3.12
	- `uv`
	- CUDA + NVIDIA GPU (sm90 class, e.g. H100/H200, per project docs)
	- Rust toolchain
	- Running `pearld` node with RPC credentials

	### Docker Example

	From the Pearl repository root:

	```bash
	docker buildx build -t vllm_miner . -f miner/vllm-miner/Dockerfile
	```

	```bash
	docker run --rm -it --gpus all \
	-p 8000:8000 -p 8337:8337 -p 8339:8339 \
	-e PEARLD_RPC_URL=<PEARLD_URL> \
	-e PEARLD_RPC_USER=<RPC_USER> \
	-e PEARLD_RPC_PASSWORD=<RPC_PASSWORD> \
	-v ~/.cache/huggingface:/root/.cache/huggingface \
	--shm-size 8g \
	vllm_miner:latest \
	pearl-ai/Llama-3.1-8B-Instruct-pearl \
	--host 0.0.0.0 --port 8000 \
	--max-model-len 8192 \
	--gpu-memory-utilization 0.9 \
	--enforce-eager
	```

	## License

	This model is a derivative of Llama 3.1 and is distributed under the Llama 3.1 license terms.

	## Limitations

	- Can generate incorrect, unsafe, or biased outputs.
	- Requires careful deployment controls and output validation.
	- Hardware/software compatibility depends on the Pearl miner stack and supported GPU architectures.