queue_merged-u207

Instructions to use eric-the-coder/queue_merged-u207 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eric-the-coder/queue_merged-u207 with Transformers:

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

pipe = pipeline("text-generation", model="eric-the-coder/queue_merged-u207")
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("eric-the-coder/queue_merged-u207")
model = AutoModelForMultimodalLM.from_pretrained("eric-the-coder/queue_merged-u207", 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 eric-the-coder/queue_merged-u207 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eric-the-coder/queue_merged-u207"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eric-the-coder/queue_merged-u207",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eric-the-coder/queue_merged-u207

SGLang

How to use eric-the-coder/queue_merged-u207 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 "eric-the-coder/queue_merged-u207" \
    --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": "eric-the-coder/queue_merged-u207",
		"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 "eric-the-coder/queue_merged-u207" \
        --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": "eric-the-coder/queue_merged-u207",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eric-the-coder/queue_merged-u207 with Docker Model Runner:
```
docker model run hf.co/eric-the-coder/queue_merged-u207
```

queue_merged-u207 / README.md

eric-the-coder

unconst

Duplicate from unconst/Affine-5czsc2fc98-r596-r252-odpo-hirank-midbeta-softctx-megaextra-merged

316de84 about 2 months ago

preview code

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2.05 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
	- r596
	---

	# 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.