Instructions to use BAAI/Infinity-Instruct-3M-0625-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BAAI/Infinity-Instruct-3M-0625-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="BAAI/Infinity-Instruct-3M-0625-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BAAI/Infinity-Instruct-3M-0625-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("BAAI/Infinity-Instruct-3M-0625-Mistral-7B")
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 BAAI/Infinity-Instruct-3M-0625-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/Infinity-Instruct-3M-0625-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/Infinity-Instruct-3M-0625-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BAAI/Infinity-Instruct-3M-0625-Mistral-7B

SGLang

How to use BAAI/Infinity-Instruct-3M-0625-Mistral-7B 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 "BAAI/Infinity-Instruct-3M-0625-Mistral-7B" \
    --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": "BAAI/Infinity-Instruct-3M-0625-Mistral-7B",
		"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 "BAAI/Infinity-Instruct-3M-0625-Mistral-7B" \
        --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": "BAAI/Infinity-Instruct-3M-0625-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BAAI/Infinity-Instruct-3M-0625-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/BAAI/Infinity-Instruct-3M-0625-Mistral-7B
```

Add pipeline tag, library name, link to paper and Github repository

by nielsr HF Staff - opened Jun 16, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-5

Files changed (1) hide show

README.md +6 -5

README.md CHANGED Viewed

@@ -1,10 +1,13 @@
 ---
-license: apache-2.0
 datasets:
 - BAAI/Infinity-Instruct
 language:
 - en
 ---
 # Infinity Instruct
 <p align="center">
@@ -12,7 +15,7 @@ language:
 </p>
 <p align="center">
 <em>Beijing Academy of Artificial Intelligence (BAAI)</em><br/>
-<em>[Paper][Code][🤗] (would be released soon)</em>
 </p>
 Infinity-Instruct-3M-0625-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-3M and Infinity-Instruct-0625](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x7B v0.1, Gemini Pro, and GPT-3.5.
@@ -133,8 +136,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 ## **Disclaimer**
 The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of Infinity Instruct is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
@@ -151,4 +152,4 @@ Our paper, detailing the development and features of the **Infinity Instruct** d
   journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
 }
-```

 ---
 datasets:
 - BAAI/Infinity-Instruct
 language:
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # Infinity Instruct
 <p align="center">
 </p>
 <p align="center">
 <em>Beijing Academy of Artificial Intelligence (BAAI)</em><br/>
+<em>[Paper](https://huggingface.co/papers/2506.11116)[Code](https://github.com/FlagOpen/FlagScale)[\ud83e\udd17] (would be released soon)</em>
 </p>
 Infinity-Instruct-3M-0625-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-3M and Infinity-Instruct-0625](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x7B v0.1, Gemini Pro, and GPT-3.5.
 print(response)
 ```
 ## **Disclaimer**
 The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of Infinity Instruct is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
   journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
 }
+```