Instructions to use Qwen/Qwen2.5-7B-Instruct-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2.5-7B-Instruct-1M with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen2.5-7B-Instruct-1M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct-1M")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct-1M", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen2.5-7B-Instruct-1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2.5-7B-Instruct-1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen2.5-7B-Instruct-1M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen2.5-7B-Instruct-1M

SGLang

How to use Qwen/Qwen2.5-7B-Instruct-1M 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 "Qwen/Qwen2.5-7B-Instruct-1M" \
    --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": "Qwen/Qwen2.5-7B-Instruct-1M",
		"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 "Qwen/Qwen2.5-7B-Instruct-1M" \
        --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": "Qwen/Qwen2.5-7B-Instruct-1M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen2.5-7B-Instruct-1M with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2.5-7B-Instruct-1M
```

Did the base 1M start from qwen2.5-7b?

by huu-ontocord - opened Feb 6, 2025

Discussion

huu-ontocord

Feb 6, 2025

Did you extend the qwen 2.5-7b to 1M and then did instruct? Is there any relation to qwen 2.5 7b

huu-ontocord

Feb 6, 2025

Specifically in the paper:
Qwen2.5-1M series are developed based on Qwen2.5 models (Yang et al., 2025) and support context
length up to 1M tokens.

huu-ontocord

Feb 6, 2025

And this:
The first two stages are similar to those of other Qwen2.5 models, where we directly use an intermediate
version from Qwen2.5 Base models for subsequent long-context training. Specifically, the model is initially
trained with a context length of 4096 tokens, and then the training is transferred to a context length of
32768 tokens. D

hzhwcmhf

Qwen org Feb 9, 2025

@huu-ontocord As described in the paper, we use an intermediate version of Qwen2.5 Base models. It reverts to a state several billion tokens before finishing the 32k training of Qwen2.5-7/14B, prior to the learning rate becoming too small.

huu-ontocord

Feb 9, 2025

That makes sense. Thank you very much for your answer!

huu-ontocord changed discussion status to closed Feb 9, 2025

Ghost04m

Feb 12, 2025

Skbaai

Feb 17, 2025

Skbaai

Feb 17, 2025

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