Instructions to use jinaai/ReaderLM-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jinaai/ReaderLM-v2 with Transformers:

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

pipe = pipeline("text-generation", model="jinaai/ReaderLM-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jinaai/ReaderLM-v2")
model = AutoModelForCausalLM.from_pretrained("jinaai/ReaderLM-v2", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jinaai/ReaderLM-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jinaai/ReaderLM-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinaai/ReaderLM-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jinaai/ReaderLM-v2

SGLang

How to use jinaai/ReaderLM-v2 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 "jinaai/ReaderLM-v2" \
    --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": "jinaai/ReaderLM-v2",
		"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 "jinaai/ReaderLM-v2" \
        --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": "jinaai/ReaderLM-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jinaai/ReaderLM-v2 with Docker Model Runner:
```
docker model run hf.co/jinaai/ReaderLM-v2
```

add AIBOM

#15

by sabato-nocera - opened Jun 14, 2025

base: refs/heads/main

←

from: refs/pr/15

Discussion Files changed

+61

-0

Files changed (1) hide show

jinaai_ReaderLM-v2.json +61 -0

jinaai_ReaderLM-v2.json ADDED Viewed

	@@ -0,0 +1,61 @@

+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:aad44aae-139c-4e30-aa11-1f1db37ed860",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:42:10.771370+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "jinaai/ReaderLM-v2-8062f244-49b2-59f3-b3e0-a21ed2b2fd33",
+            "name": "jinaai/ReaderLM-v2",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/jinaai/ReaderLM-v2",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "qwen2",
+                    "modelArchitecture": "Qwen2ForCausalLM"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "jinaai"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "CC-BY-NC-4.0",
+                        "url": "https://spdx.org/licenses/CC-BY-NC-4.0.html"
+                    }
+                }
+            ],
+            "description": "- **Model Type**: Autoregressive, decoder-only transformer- **Parameter Count**: 1.54B- **Context Window**: Up to 512K tokens (combined input and output)- **Hidden Size**: 1536- **Number of Layers**: 28- **Query Heads**: 12- **KV Heads**: 2- **Head Size**: 128- **Intermediate Size**: 8960- **Supported Languages**: English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic, and more (29 total)---",
+            "tags": [
+                "transformers",
+                "onnx",
+                "safetensors",
+                "qwen2",
+                "text-generation",
+                "conversational",
+                "multilingual",
+                "arxiv:2503.01151",
+                "license:cc-by-nc-4.0",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "region:eu"
+            ]
+        }
+    }
+}