Instructions to use elevateecho/sn120-9fbb78acd964 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elevateecho/sn120-9fbb78acd964 with Transformers:

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

pipe = pipeline("image-text-to-text", model="elevateecho/sn120-9fbb78acd964")
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/sn120-9fbb78acd964")
model = AutoModelForMultimodalLM.from_pretrained("elevateecho/sn120-9fbb78acd964", 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/sn120-9fbb78acd964 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "elevateecho/sn120-9fbb78acd964"
# 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/sn120-9fbb78acd964",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/elevateecho/sn120-9fbb78acd964

SGLang

How to use elevateecho/sn120-9fbb78acd964 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/sn120-9fbb78acd964" \
    --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/sn120-9fbb78acd964",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/sn120-9fbb78acd964" \
        --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/sn120-9fbb78acd964",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use elevateecho/sn120-9fbb78acd964 with Docker Model Runner:
```
docker model run hf.co/elevateecho/sn120-9fbb78acd964
```

Affine SN120 candidate p28 (HiAlpha-GRPO on p24 / t6)

Reason-v4 GRPO checkpoint trained in the /mining/ralph loop on an 8×H100 PCIe box. Pushed for a live SN120 duel against the sitting king vera6/affine-5g4yy75zuz-t6 @ 8e3f1695e058837ed80fec3238ff439fdc2d0f0e (reign 36). Architecture is stock Qwen3_5MoeForConditionalGeneration (hidden 2048, 40 layers, 256 experts / 8 active). No custom modeling code, no auto_map, no *.py.

Lineage

Live king vera6/affine-5g4yy75zuz-t6 @ 8e3f1695e058837ed80fec3238ff439fdc2d0f0e
p24 — HiAlpha-GRPO on that king (cand_p24-grpo-king-affine-5g4yy75zu). Local v4 screen vs t6: n=400, margin −0.00009, z −0.09, med |z| 139, B-pass 0.46 (tie / BELOW_BAR).
p28 — same recipe on p24 (this repo). Merged LoRA → bf16 safetensors via /mining/sim/merge_lora2.py (nonzero delta + 333 visual tensors).

Experiment path: /mining/ralph/runs/p28-grpo-p24-grpo-king-affine-5g4

Method

Teacher-anchored Reason v4 GRPO (train_reason_grpo.py).

Per-sample reward:

a_i    = lpC(y_i | z_A) − lpC(y_i | ∅)     # k=3 teacher refs
Reason = τ · log((1/k) · Σ exp(a_i/τ))     # τ=0.03
reward = Reason + length_shape(|z|)        # penalize |z|≥220 only

Winner-only tail-boost 2.0 on the best group member. Ranked quantity is the thought z (action y is not the score). Teacher is the frozen Affine teacher zai-org/GLM-4.5-Air-FP8, two local vLLM TP=2 endpoints.

Data

Pool: /mining/ralph/data/grpo.jsonl
Kept 871 / 1200 rows (char budget 12288)
Affine public turn corpus D (SWE-style coding turns), same chat contract the validator uses (THOUGHT + last closed bash fence)

Hyperparameters

knob	value
method	HiAlpha-GRPO (LoRA)
lr	5e-6
LoRA r / α / dropout	16 / 128 / 0.05
target modules	q,k,v,o,gate,up,down `_proj`
group size G	4
steps	200
max new tokens (z sample)	64
max seq	6144
KL coef	0.0
tail-boost	2.0 winner-only
length shape	penalty on \|z\|≥220, no length bonus
τ / k_refs	0.03 / 3
dtype	bfloat16

Train wall: 4524 s (~75 min). Last-20 mean reward 0.044. Trainable 8.36M / 34.7B (0.024%). GPUs 4–7 for LoRA (device_map=auto); teachers on 0–1 and 2–3.

Local sim vs live king (Reason v4, k=3, τ=0.03)

Screened 2026-08-19 against the same king SHA still sitting at push time (vera6/affine-5g4yy75zuz-t6 @ 8e3f1695…). gate_screen.py / fast_screen n=160:

	cand	king
mean Reason	0.01184	0.00995
med \|z\|	144	145
B-pass	0.46	0.49

margin +0.00189 · SE 0.00149 · z +1.27 · n=160
win_frac 0.40 · median Δ 0 · p90 Δ +0.00943 · top-5% contrib +0.00288
gates: thought length PASS (≥80), causality B PASS (≥0.30)
live crown bar = max(2·SE, δ=0.002) ≈ 0.0030 on this slice
local verdict: BELOW_BAR (n=160 is a rejector vs live n_turns=1300)

p28 was the closest this-loop candidate vs t6. Live duel is n=1300 k=3; this n=160 slice does not prove a crown.

Serving

Same family the eval pod loads: Qwen3.5-MoE, canonical sharded safetensors (model-00001-of-00002 + model-00002-of-00002 + model.safetensors.index.json

model-visual.safetensors). No --trust-remote-code. Local screen served this merge with vLLM TP=2.

Hardware

8× NVIDIA H100 80GB PCIe. Merge on CPU (device_map=cpu).

Downloads last month: 6

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for elevateecho/sn120-9fbb78acd964

Base model

vera6/affine-5g4yy75zuz-t6

Finetuned

(63)

this model