Instructions to use elevateecho/sn120-3a778f1fd066 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elevateecho/sn120-3a778f1fd066 with Transformers:

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

pipe = pipeline("text-generation", model="elevateecho/sn120-3a778f1fd066")
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("elevateecho/sn120-3a778f1fd066")
model = AutoModelForMultimodalLM.from_pretrained("elevateecho/sn120-3a778f1fd066", 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 elevateecho/sn120-3a778f1fd066 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "elevateecho/sn120-3a778f1fd066"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "elevateecho/sn120-3a778f1fd066",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/elevateecho/sn120-3a778f1fd066

SGLang

How to use elevateecho/sn120-3a778f1fd066 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 "elevateecho/sn120-3a778f1fd066" \
    --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": "elevateecho/sn120-3a778f1fd066",
		"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 "elevateecho/sn120-3a778f1fd066" \
        --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": "elevateecho/sn120-3a778f1fd066",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use elevateecho/sn120-3a778f1fd066 with Docker Model Runner:
```
docker model run hf.co/elevateecho/sn120-3a778f1fd066
```

R596 — SoftCtx × HiRank × MidBeta MegaExtra (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 × HiRank pair set (soft context length band, high-rank filter); MidBeta β=0.1. See experiment plan.md / dpo_duel_reason.jsonl under mining/experiments/r596-r252-offline-dpo-hialpha-hirank-midbeta-softctx-megaextrasteps.
Key hyperparameters:
- LoRA r=64, α=128
- β=0.1 (MidBeta)
- lr=5e-6
- max_len=12288 (SoftCtx)
- max_steps target 3600 MegaExtra; TRAIN_DONE@259 (adapter kept / merged)
Hardware: Lium mine-r226-marsplan-fullft-1 (brave) GPUs 6,7 for train; merge + n80 on mine-r252-vera-t4-nonking-grpo-1 GPUs 4,5 → /tmp/r596_merged
Local n80 vs live king reign34 (cryptoDev23/Affine-5Dku3dYp9j-hk8161@55b7ffe0…):
- margin +0.006196, SE 0.002357, z=2.63, n=75
- bar max(2·SE, δ=0.002) = 0.004713 (~1.31×)
- thought median 199 (≥80), B pass 0.368 (≥0.30)
- decision: Stage-5 licensed (r596_decision_reign34.json)
Prior n80 vs r252: margin +0.008490 (~1.19× bar), thought/B clear
Experiment path: mining/experiments/r596-r252-offline-dpo-hialpha-hirank-midbeta-softctx-megaextrasteps

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 elevateecho/sn120-3a778f1fd066

Base model

Ichiro1007/Affine-chal-00627

Finetuned

unconst/Affine-5czsc2fc98-r252-merged

Finetuned

(36)

this model