Instructions to use ShellFace/20260820-124720 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShellFace/20260820-124720 with Transformers:

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

pipe = pipeline("text-generation", model="ShellFace/20260820-124720")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ShellFace/20260820-124720")
model = AutoModelForMultimodalLM.from_pretrained("ShellFace/20260820-124720", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ShellFace/20260820-124720 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ShellFace/20260820-124720"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ShellFace/20260820-124720",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ShellFace/20260820-124720

SGLang

How to use ShellFace/20260820-124720 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 "ShellFace/20260820-124720" \
    --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": "ShellFace/20260820-124720",
		"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 "ShellFace/20260820-124720" \
        --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": "ShellFace/20260820-124720",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ShellFace/20260820-124720 with Docker Model Runner:
```
docker model run hf.co/ShellFace/20260820-124720
```

R1008 — MidCtx × HiRank × MidBeta Mega MidLR (offline DPO on vera king)

Affine SN120 challenger for Reason v4 (weight_version_key=7): tempered multi-sample log-mean-exp over k=3 teacher refs (τ=0.03).

Per turn: a_i = lpC(y_i|z_A) − lpC(y_i|∅); Reason = τ·log(mean_i exp(a_i/τ)). Crown also needs median stripped |z|≥80 and B pass ≥0.30.

How this checkpoint was trained

Base / parent: vera6/affine-5g4yy75zuz-t6@8e3f1695e058837ed80fec3238ff439fdc2d0f0e (live king reign36)
Method: offline DPO on Reason-ranked duel pairs (not SFT / not online GRPO)
What was optimized: preference for thoughts that raise teacher-side Reason (commit to a teacher next-action mode; filler loses under LME)
Data: Soft Mid Mid Soft × MidCtx filtered duel preference pairs from dpo_duel_reason.jsonl under mining/experiments/r1008-vera-offline-dpo-hialpha-hirank-midbeta-midctx-megasuperextrasteps-ep4-midlr / pod /root/r1008/
Key hyperparameters:
- LoRA r=64 (HiRank), α=128 (HiAlpha)
- β=0.1 (MidBeta)
- lr=1e-6 (MidLR)
- max_len=8192 (MidCtx)
- max_steps=19200 (MegaSuperExtra)
- epochs=4
Hardware: Lium mine-r338-marsplan-online-dpo-bigg-hilr-1 (calm-fox-6a) 8×B200 GPUs 4,5 train+merge; TKC warm; chall :8003 GPUs 4,5 for v4 n80 → /tmp/r1008_merged (~16 safetensor shards)
Local n80 vs live king reign36 (vera6/affine-5g4yy75zuz-t6@8e3f1695e058837ed80fec3238ff439fdc2d0f0e) under wvk=7:
- margin +0.005917, SE 0.002233, z=2.650, n=80
- bar max(2·SE, δ=0.002) = 0.004466 (~1.325×)
- thought median 165 (≥80 ✓), B pass 0.433 (≥0.30 ✓)
- k=3, τ=0.03 (fail-closed if stamp ≠ v4)
- decision: WIN / Stage-5 licensed (r1008_sim_result_reign36_wvk7.json, p4150)
Lineage: R1000 MidCtx HiRank Midβ Mega UltraLoLR REFUTE m=−0.000317 ~−0.15× → MidCtx HiRank Midβ Mega MidLR isolate between UltraLoLR R1000 and HiLR R991; ≠ Mega UltraLoLR R1000 / ≠ Mega HiLR R991 / ≠ SoftCtx Mega UltraLoLR R959 / ≠ SoftCtx HiRank Midβ Mega MidLR R992 / ≠ Online / ≠ GRPO
Experiment path: mining/experiments/r1008-vera-offline-dpo-hialpha-hirank-midbeta-midctx-megasuperextrasteps-ep4-midlr

Intended use

SN120 Affine miner submission / evalsrv Reason v4 duel. Not a general chat model.

License

Follows base model + Affine mining artifacts policy.

Downloads last month: 191

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for ShellFace/20260820-124720

Base model

vera6/affine-5g4yy75zuz-t6

Finetuned

(70)

this model