Instructions to use Cheeeeeeeeky/affine-gone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cheeeeeeeeky/affine-gone with Transformers:

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

pipe = pipeline("text-generation", model="Cheeeeeeeeky/affine-gone")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cheeeeeeeeky/affine-gone")
model = AutoModelForCausalLM.from_pretrained("Cheeeeeeeeky/affine-gone")
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 Cheeeeeeeeky/affine-gone with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Cheeeeeeeeky/affine-gone

SGLang

How to use Cheeeeeeeeky/affine-gone 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 "Cheeeeeeeeky/affine-gone" \
    --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": "Cheeeeeeeeky/affine-gone",
		"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 "Cheeeeeeeeky/affine-gone" \
        --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": "Cheeeeeeeeky/affine-gone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Cheeeeeeeeky/affine-gone with Docker Model Runner:
```
docker model run hf.co/Cheeeeeeeeky/affine-gone
```

INTELLECT-3

INTELLECT-3: A 100B+ MoE trained with large-scale RL

Trained with prime-rl and verifiers
Environments released on Environments Hub
Read the Blog & Technical Report
X | Discord | Prime Intellect Platform

Introduction

INTELLECT-3 is a 106B (A12B) parameter Mixture-of-Experts reasoning model post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL).

Training was performed with prime-rl using environments built with the verifiers library. All training and evaluation environments are available on the Environments Hub.

The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).

For more details, see the technical report.

Evaluation

INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:

Benchmark	MATH-500	AIME24	AIME25	LCB	GPQA	HLE	MMLU-Pro
INTELLECT-3	98.1	90.8	88.0	69.3	74.4	14.6	81.9
GLM-4.5-Air	97.8	84.6	82.0	61.5	73.3	13.3	73.9
GLM-4.5	97.0	85.8	83.3	64.5	77.0	14.8	83.5
DeepSeek R1 0528	87.3	83.2	73.4	62.5	77.5	15.9	75.3
DeepSeek v3.2	96.8	88.1	84.7	71.6	81.4	17.9	84.6
GPT-O5S 120B	96.0	75.8	77.7	69.9	70.0	10.6	67.1

Model Variants

Model	HuggingFace
INTELLECT-3	PrimeIntellect/INTELLECT-3
INTELLECT-3-FP8	PrimeIntellect/INTELLECT-3-FP8

Serving with vLLM

The BF16 version can be served on 2x H200s:

vllm serve PrimeIntellect/INTELLECT-3 \
    --tensor-parallel-size 2 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

The FP8 version can be served on a single H200:

vllm serve PrimeIntellect/INTELLECT-3-FP8 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

Citation

@misc{intellect3,
  title={INTELLECT-3: Technical Report},
  author={Prime Intellect Team},
  year={2025},
  url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}

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Safetensors

Model size

107B params

Tensor type

F32

BF16

F8_E4M3

Model tree for Cheeeeeeeeky/affine-gone

Base model

zai-org/GLM-4.5-Air-Base

Quantized

(5)

this model