Instructions to use HelpingAI/hai3.1-checkpoint-0002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/hai3.1-checkpoint-0002 with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/hai3.1-checkpoint-0002", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HelpingAI/hai3.1-checkpoint-0002", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HelpingAI/hai3.1-checkpoint-0002 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/hai3.1-checkpoint-0002"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HelpingAI/hai3.1-checkpoint-0002",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/hai3.1-checkpoint-0002

SGLang

How to use HelpingAI/hai3.1-checkpoint-0002 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 "HelpingAI/hai3.1-checkpoint-0002" \
    --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": "HelpingAI/hai3.1-checkpoint-0002",
		"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 "HelpingAI/hai3.1-checkpoint-0002" \
        --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": "HelpingAI/hai3.1-checkpoint-0002",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HelpingAI/hai3.1-checkpoint-0002 with Docker Model Runner:
```
docker model run hf.co/HelpingAI/hai3.1-checkpoint-0002
```

Vortexjr commited on Aug 14, 2025

Commit

697c3b9

verified ·

1 Parent(s): dd7d18d

Update README.md

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README.md CHANGED Viewed

@@ -47,7 +47,7 @@ model.generate(
     streamer=streamer
 )
 ```
-Classfication section undertraining
 ```py
 import os
 import json
@@ -55,7 +55,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # Path to saved model (change if needed)
-ckpt = "HelpingAI/hai3.1-checkpoint-0002"  # or the HF name like HelpingAI/hai3.1-checkpoint-0001
 device = "cpu"
 print("Device:", device)
@@ -104,7 +104,6 @@ print("Predicted label:", id2label[pred_id] if pred_id < len(id2label) else "unk
 TTS layers in training
-NOTE: we have used qwen2 tokenizer in it
 This model contains layers from our diffrent models
-To aline layers we have done post training after merging layers

     streamer=streamer
 )
 ```
+Classfication
 ```py
 import os
 import json
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # Path to saved model (change if needed)
+ckpt = "HelpingAI/hai3.1-checkpoint-0002"
 device = "cpu"
 print("Device:", device)
 TTS layers in training
 This model contains layers from our diffrent models
+To aline layers we have done post-training after merging layers