Instructions to use jinaai/reader-lm-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jinaai/reader-lm-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="jinaai/reader-lm-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jinaai/reader-lm-1.5b")
model = AutoModelForCausalLM.from_pretrained("jinaai/reader-lm-1.5b")
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

vLLM

How to use jinaai/reader-lm-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jinaai/reader-lm-1.5b"
# 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/reader-lm-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jinaai/reader-lm-1.5b

SGLang

How to use jinaai/reader-lm-1.5b 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/reader-lm-1.5b" \
    --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/reader-lm-1.5b",
		"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/reader-lm-1.5b" \
        --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/reader-lm-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jinaai/reader-lm-1.5b with Docker Model Runner:
```
docker model run hf.co/jinaai/reader-lm-1.5b
```

Hello world example got wrong output

by AIRDGempoll - opened Sep 20, 2024

Discussion

AIRDGempoll

Sep 20, 2024

Hi I am running the hello world example in your doc, but I think the output is sort of wrong.

# in ipython
In [4]: html_content = "<html><body><h1>Hello, world!</h1></body></html>"
   ...: 
   ...: messages = [{"role": "user", "content": html_content}]
   ...: input_text=tokenizer.apply_chat_template(messages, tokenize=False)
   ...: 
   ...: print(input_text)
   ...: 
   ...: inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
   ...: outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)
   ...: 
   ...: print(tokenizer.decode(outputs[0]))
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
<html><body><h1>Hello, world!</h1></body></html><|im_end|>

/home/ai/mambaforge/envs/readerlm/lib/python3.12/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/ai/mambaforge/envs/readerlm/lib/python3.12/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(
/home/ai/mambaforge/envs/readerlm/lib/python3.12/site-packages/transformers/generation/configuration_utils.py:589: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `20` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`.
  warnings.warn(
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
<html><body><h1>Hello, world!</h1></body></html><|im_end|>
<|im_start|>assistant
Hello, world!<|im_end|>

I am expecting the output of assistant to be # Hello, world!

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