Instructions to use athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit")
model = AutoModelForCausalLM.from_pretrained("athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit")
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 athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit

SGLang

How to use athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit 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 "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit" \
    --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": "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit",
		"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 "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit" \
        --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": "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit with Docker Model Runner:
```
docker model run hf.co/athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit
```

DeepSeek-Prover-V2-7B - GPTQ 4-bit

Community GPTQ int4 quantization of deepseek-ai/DeepSeek-Prover-V2-7B for Lean-4 theorem proving on consumer GPUs (tested on RTX 3080, 10 GB VRAM).

Weights: 4.6 GB (vs 14 GB FP16) - fits inference on 8-10 GB cards with room for generation context.

Quantization config

Bits: 4
Group size: 128
Symmetry: asymmetric (sym=False)
Order: true-sequential
Damp: 0.01
Calibration: pileval, 128 samples x 512 tokens
Tool: auto-gptq 0.7.1

Why GPTQ instead of AWQ on this model

AutoAWQ (0.2.9) crashed at layer 17 of 30 on this card with NotImplementedError: Cannot copy out of meta tensor - the per-layer scale search collides with accelerate's CPU-offload hooks on sub-VRAM quantizes. auto-gptq's per-layer lift-quantize-drop pattern avoids that path. Details in the recipe blog post (link TBD once published).

Load example

from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer

model_id = "athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit"  # or the fallback user namespace
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoGPTQForCausalLM.from_quantized(
    model_id, device="cuda:0", use_safetensors=True, trust_remote_code=False,
)

Observed greedy throughput on RTX 3080 (triton kernels, no compiled CUDA ext): ~3.9 tok/s at 32 new tokens.

Known caveats

auto-gptq 0.7.1 requires peft<0.11 - newer peft removed PEFT_TYPE_TO_MODEL_MAPPING which breaks the import.
rope_scaling warnings on load are cosmetic (HF transformers 4.51.3 nits), safe to ignore.
Capability-preservation vs FP16 is workload-dependent. Benchmark your own suite before deploying. Calibration was pileval (generic); a Lean-specific calibration corpus may improve close-rate for theorem proving.

Dependencies used to produce this upload

torch==2.6.0+cu124
transformers==4.51.3
auto-gptq==0.7.1
peft<0.11
accelerate==1.13.0
safetensors==0.7.0

License + credit

Inherits from upstream DeepSeek-Prover-V2 Model License (DeepSeek License Agreement v1.0) - see license_link in the card metadata. Original model and training: DeepSeek. This quantization: athanor-ai.

Companion benchmark

Evaluated against athanor-ai/formal-anytime-valid-stats-48 — a 48-theorem Lean-4 benchmark of anytime-valid confidence-sequence lemmas. See the dataset README for per-theorem close rates of this model alongside Claude Opus 4.6, Sonnet 4.6, Kimi K2.5, and Gemini 3 Pro.

Downloads last month: 74

Safetensors

Model size

7B params

Tensor type

I32

F16

Model tree for athanor-ai/DeepSeek-Prover-V2-7B-GPTQ-4bit

Base model

deepseek-ai/DeepSeek-Prover-V2-7B

Quantized

(15)

this model