Instructions to use pearl-ai/Llama-3.3-70B-Instruct-pearl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pearl-ai/Llama-3.3-70B-Instruct-pearl with Transformers:

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

pipe = pipeline("text-generation", model="pearl-ai/Llama-3.3-70B-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-3.3-70B-Instruct-pearl")
model = AutoModelForCausalLM.from_pretrained("pearl-ai/Llama-3.3-70B-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-3.3-70B-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-3.3-70B-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-3.3-70B-Instruct-pearl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pearl-ai/Llama-3.3-70B-Instruct-pearl

SGLang

How to use pearl-ai/Llama-3.3-70B-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-3.3-70B-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-3.3-70B-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-3.3-70B-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-3.3-70B-Instruct-pearl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pearl-ai/Llama-3.3-70B-Instruct-pearl with Docker Model Runner:
```
docker model run hf.co/pearl-ai/Llama-3.3-70B-Instruct-pearl
```

netanell-pearl commited on Apr 27

Commit

6cc401c

verified ·

1 Parent(s): 3891d67

Add public-safe model card with Pearl vLLM usage

Browse files

Files changed (1) hide show

README.md +51 -2

README.md CHANGED Viewed

@@ -7,15 +7,24 @@ pipeline_tag: text-generation
 tags:
 - pearl
 - llama
 - instruct
 - large-language-model
 - quantization
 base_model: meta-llama/Llama-3.3-70B-Instruct
 ---
-# Llama-3.3-70B-Instruct-pearl
-Launch details and benchmark source: [pearlresearch.ai/#launch](https://pearlresearch.ai/#launch)
 Original (Meta's) llama-3.3-70B-Instruct vs. our "two-for-one" Pearl-certified variant. Both executions were done with 4xH200 GPUs. We explore several parallelism techniques. TMADs, i.e., Tera MADs, is a metric counting number of Multiply-Add (MAD) operations. Useful MADs is the total number of MAD operations done anyway that are used for mining.
@@ -84,3 +93,43 @@ Original (Meta's) llama-3.3-70B-Instruct vs. our "two-for-one" Pearl-certified v
     </tr>
   </tbody>
 </table>

 tags:
 - pearl
 - llama
+- llama-3.3
 - instruct
 - large-language-model
 - quantization
+- vllm
+- mining
 base_model: meta-llama/Llama-3.3-70B-Instruct
 ---
+# pearl-ai/Llama-3.3-70B-Instruct-pearl
+Pearl-certified variant of Llama-3.3-70B-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)
+## Launch Benchmark
 Original (Meta's) llama-3.3-70B-Instruct vs. our "two-for-one" Pearl-certified variant. Both executions were done with 4xH200 GPUs. We explore several parallelism techniques. TMADs, i.e., Tera MADs, is a metric counting number of Multiply-Add (MAD) operations. Useful MADs is the total number of MAD operations done anyway that are used for mining.
     </tr>
   </tbody>
 </table>
+## How To Use (Pearl vLLM Plugin)
+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.
+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-3.3-70B-Instruct-pearl \
+  --host 0.0.0.0 --port 8000 \
+  --max-model-len 8192 \
+  --gpu-memory-utilization 0.9 \
+  --enforce-eager
+```