queue_merged-u112

Instructions to use austin-n-sam/queue_merged-u112 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use austin-n-sam/queue_merged-u112 with Transformers:

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

pipe = pipeline("text-generation", model="austin-n-sam/queue_merged-u112")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("austin-n-sam/queue_merged-u112")
model = AutoModelForMultimodalLM.from_pretrained("austin-n-sam/queue_merged-u112", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use austin-n-sam/queue_merged-u112 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "austin-n-sam/queue_merged-u112"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "austin-n-sam/queue_merged-u112",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/austin-n-sam/queue_merged-u112

SGLang

How to use austin-n-sam/queue_merged-u112 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 "austin-n-sam/queue_merged-u112" \
    --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": "austin-n-sam/queue_merged-u112",
		"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 "austin-n-sam/queue_merged-u112" \
        --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": "austin-n-sam/queue_merged-u112",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use austin-n-sam/queue_merged-u112 with Docker Model Runner:
```
docker model run hf.co/austin-n-sam/queue_merged-u112
```

queue_merged-u112 / README.md

austin-n-sam

Duplicate from unconst/Affine-5czsc2fc98-r637-r252-odpo-midrank-lobeta-softctx-ep3-lolr-merged

c1739b3 3 months ago

preview code

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2.16 kB

---
base_model: unconst/Affine-5czsc2fc98-r252-merged
base_model_revision: b42d6245d77fe30885ea8a90387771e1bc465e0f
library_name: transformers
pipeline_tag: text-generation
tags:
  - affine
  - sn120
  - reason-v3
  - offline-dpo
  - r637
---
# 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.

	---
	base_model: unconst/Affine-5czsc2fc98-r252-merged
	base_model_revision: b42d6245d77fe30885ea8a90387771e1bc465e0f
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- affine
	- sn120
	- reason-v3
	- offline-dpo
	- r637
	---

	# 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.