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

Libraries

How to use BAAI/Infinity-Instruct-7M-Gen-mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="BAAI/Infinity-Instruct-7M-Gen-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-7M-Gen-mistral-7B")
model = AutoModelForCausalLM.from_pretrained("BAAI/Infinity-Instruct-7M-Gen-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

docker model run hf.co/BAAI/Infinity-Instruct-7M-Gen-mistral-7B

SGLang

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

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

-6

Files changed (1) hide show

README.md +9 -6

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-7M-Gen-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-7M and Infinity-Instruct-Gen](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x22B v0.1, Gemini Pro, and GPT-4.
@@ -74,7 +77,7 @@ Thanks to [FlagScale](https://github.com/FlagOpen/FlagScale), we could concatena
 ## **How to use**
-Infinity-Instruct-7M-Gen-Mistral-7B adopt the same chat template of [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B):
 ```bash
 <|im_start|>system
@@ -144,13 +147,13 @@ The resources, including code, data, and model weights, associated with this pro
 ##
 ## **Citation**
-Our paper, detailing the development and features of the **Infinity Instruct** dataset and finetuned models, will be released soon on arXiv. Stay tuned!
 ```
 @article{InfinityInstruct2024,
-  title={Infinity Instruct},
   author={Beijing Academy of Artificial Intelligence (BAAI)},
-  journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
 }
 ```

 ---
 datasets:
 - BAAI/Infinity-Instruct
 language:
 - en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # 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/BAAI/Infinity-Instruct)[\ud83e\udd17] (would be released soon)</em>
 </p>
 Infinity-Instruct-7M-Gen-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-7M and Infinity-Instruct-Gen](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x22B v0.1, Gemini Pro, and GPT-4.
 ## **How to use**
+Infinity-Instruct-7M-Gen-Mistral-7B adopt the same chat template of [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B):\
 ```bash
 <|im_start|>system
 ##
 ## **Citation**
+Our paper, detailing the development and features of the **Infinity Instruct** dataset and finetuned models, has been released on arXiv:
 ```
 @article{InfinityInstruct2024,
+  title={Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models},
   author={Beijing Academy of Artificial Intelligence (BAAI)},
+  journal={arXiv preprint arXiv:2506.11116},
   year={2024}
 }
 ```