unc-8d257e23

Instructions to use wind77/unc-8d257e23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wind77/unc-8d257e23 with Transformers:

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

pipe = pipeline("text-generation", model="wind77/unc-8d257e23")
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("wind77/unc-8d257e23")
model = AutoModelForMultimodalLM.from_pretrained("wind77/unc-8d257e23", 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 wind77/unc-8d257e23 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wind77/unc-8d257e23"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wind77/unc-8d257e23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wind77/unc-8d257e23

SGLang

How to use wind77/unc-8d257e23 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 "wind77/unc-8d257e23" \
    --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": "wind77/unc-8d257e23",
		"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 "wind77/unc-8d257e23" \
        --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": "wind77/unc-8d257e23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wind77/unc-8d257e23 with Docker Model Runner:
```
docker model run hf.co/wind77/unc-8d257e23
```

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R637 — SoftCtx × MidRank × LoBeta MegaExtra ep3 × LoLR (offline DPO)

Affine SN120 challenger trained to beat the live king on Reason v3 (teacher-anchored score: lpC(y_C|z_A) − lpC(y_C|∅)).

How this checkpoint was trained

Base / parent: unconst/Affine-5czsc2fc98-r252-merged@b42d6245d77fe30885ea8a90387771e1bc465e0f (our crowned r252, reign 33)
Method: offline DPO on Reason-ranked pairs (not SFT / not online GRPO)
What was optimized: preference for higher teacher-side Reason on mined pairs
Data: SoftCtx × MidRank × LoBeta pair set (soft context band, mid LoRA rank, low DPO β). See plan.md / dpo_duel_reason.jsonl under mining/experiments/r637-r252-offline-dpo-hialpha-midrank-lobeta-softctx-megaextrasteps-ep3-lolr.
Key hyperparameters:
- LoRA r=32 (MidRank), α=128 (HiAlpha)
- β=0.02 (LoBeta)
- lr=1e-6 (LoLR)
- max_len=12288 (SoftCtx)
- max_steps=3600 MegaExtra; epochs=3
Hardware: Lium mine-r226-marsplan-fullft-1 (brave) GPUs 2,3 train+merge; host-relay → n80 on mine-r262-kevin-v5-nonking-grpo-1 (golden) GPUs 6,7 → /tmp/r637_merged
Local n80 vs live king reign34 (cryptoDev23/Affine-5Dku3dYp9j-hk8161@55b7ffe0…):
- margin +0.005735, SE 0.001973, z=2.91, n=77
- bar max(2·SE, δ=0.002) = 0.003946 (~1.45×)
- thought median 168.5 (≥80), B pass 0.4125 (≥0.30)
- decision: Stage-5 licensed (r637_decision_reign34.json, p3643)
Lineage: amplify of R580 Soft MidRank LoBeta Mega ep1 ~0.39× after R610 ep2 @5e-6 REFUTE; ≠ Soft MidRank MidBeta R635 ep3 LoLR REFUTE
Experiment path: mining/experiments/r637-r252-offline-dpo-hialpha-midrank-lobeta-softctx-megaextrasteps-ep3-lolr

Intended use

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

License

Follows base model + Affine mining artifacts policy.

Downloads last month: -

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for wind77/unc-8d257e23

Base model

Ichiro1007/Affine-chal-00627

Finetuned

unconst/Affine-5czsc2fc98-r252-merged

Finetuned

(45)

this model