20260817-213018

Instructions to use ShellFace/20260817-213018 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShellFace/20260817-213018 with Transformers:

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

pipe = pipeline("text-generation", model="ShellFace/20260817-213018")
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/20260817-213018")
model = AutoModelForMultimodalLM.from_pretrained("ShellFace/20260817-213018", 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/20260817-213018 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ShellFace/20260817-213018"
# 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/20260817-213018",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ShellFace/20260817-213018

SGLang

How to use ShellFace/20260817-213018 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/20260817-213018" \
    --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/20260817-213018",
		"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/20260817-213018" \
        --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/20260817-213018",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ShellFace/20260817-213018 with Docker Model Runner:
```
docker model run hf.co/ShellFace/20260817-213018
```

R683 — ShortCtx × MidRank × HiBeta UltraExtra ep3 × LoLR (offline DPO)

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: unconst/Affine-5czsc2fc98-r252-merged@b42d6245d77fe30885ea8a90387771e1bc465e0f (our crowned r252, reign 33)
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: ShortCtx × MidRank × HiBeta filtered duel preference pairs from dpo_duel_reason.jsonl under mining/experiments/r683-r252-offline-dpo-hialpha-midrank-hibeta-shortctx-ultraextrasteps-ep3-lolr (kept ≥200 examples at launch)
Key hyperparameters:
- LoRA r=32 (MidRank), α=128 (HiAlpha)
- β=0.3 (HiBeta)
- lr=1e-6 (LoLR)
- max_len=6144 (ShortCtx)
- max_steps=7200 (UltraExtra; 2× Mega 3600)
- epochs=3
Hardware: Lium mine-r226-marsplan-fullft-1 (brave-raven-a9) 8×B200 GPUs 6,7 train+merge; SCP → mine-r262-kevin-v5-nonking-grpo-1 (golden-comet-78) GPUs 4,5 challenger serve + v4 n80 → /tmp/r683_merged (~66G / 16 safetensor shards)
Local n80 vs live king reign34 (cryptoDev23/Affine-5Dku3dYp9j-hk8161@55b7ffe0…) under wvk=7:
- margin +0.002137, SE 0.000943, z=2.27, n=79
- bar max(2·SE, δ=0.002) = 0.002 (~1.07×)
- thought median 172 (≥80 ✓), B pass 0.304 (≥0.30 ✓, knife-edge)
- k=3, τ=0.03 (fail-closed if stamp ≠ v4)
- decision: WIN / Stage-5 licensed (r683_decision_reign34_wvk7.json, p3738)
Lineage: amplify R659 Short MidRank HiBeta Mega / R622 ~0.85× with 2× steps; ≠ R679 Short HiRank HiBeta UltraExtra / ≠ R653 Short MidRank LoBeta / ≠ R658 Short MidRank MidBeta / ≠ Online / ≠ GRPO
Experiment path: mining/experiments/r683-r252-offline-dpo-hialpha-midrank-hibeta-shortctx-ultraextrasteps-ep3-lolr

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: 268

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for ShellFace/20260817-213018

Base model

Ichiro1007/Affine-chal-00627

Finetuned

unconst/Affine-5czsc2fc98-r252-merged

Finetuned

(45)

this model