Instructions to use eric-the-coder/queue_merged-u103 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eric-the-coder/queue_merged-u103 with Transformers:

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

pipe = pipeline("text-generation", model="eric-the-coder/queue_merged-u103")
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-u103")
model = AutoModelForMultimodalLM.from_pretrained("eric-the-coder/queue_merged-u103", 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-u103 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-u103"
# 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-u103",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eric-the-coder/queue_merged-u103

SGLang

How to use eric-the-coder/queue_merged-u103 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-u103" \
    --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-u103",
		"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-u103" \
        --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-u103",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eric-the-coder/queue_merged-u103 with Docker Model Runner:
```
docker model run hf.co/eric-the-coder/queue_merged-u103
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

R1008 — MidCtx × HiRank × MidBeta Mega MidLR (offline DPO on vera king)

Affine SN120 challenger for Reason v4 (weight_version_key=7): tempered multi-sample log-mean-exp over k=3 teacher refs (τ=0.03).

Per turn: a_i = lpC(y_i|z_A) − lpC(y_i|∅); Reason = τ·log(mean_i exp(a_i/τ)). Crown also needs median stripped |z|≥80 and B pass ≥0.30.

How this checkpoint was trained

Base / parent: vera6/affine-5g4yy75zuz-t6@8e3f1695e058837ed80fec3238ff439fdc2d0f0e (live king reign36)
Method: offline DPO on Reason-ranked duel pairs (not SFT / not online GRPO)
What was optimized: preference for thoughts that raise teacher-side Reason (commit to a teacher next-action mode; filler loses under LME)
Data: Soft Mid Mid Soft × MidCtx filtered duel preference pairs from dpo_duel_reason.jsonl under mining/experiments/r1008-vera-offline-dpo-hialpha-hirank-midbeta-midctx-megasuperextrasteps-ep4-midlr / pod /root/r1008/
Key hyperparameters:
- LoRA r=64 (HiRank), α=128 (HiAlpha)
- β=0.1 (MidBeta)
- lr=1e-6 (MidLR)
- max_len=8192 (MidCtx)
- max_steps=19200 (MegaSuperExtra)
- epochs=4
Hardware: Lium mine-r338-marsplan-online-dpo-bigg-hilr-1 (calm-fox-6a) 8×B200 GPUs 4,5 train+merge; TKC warm; chall :8003 GPUs 4,5 for v4 n80 → /tmp/r1008_merged (~16 safetensor shards)
Local n80 vs live king reign36 (vera6/affine-5g4yy75zuz-t6@8e3f1695e058837ed80fec3238ff439fdc2d0f0e) under wvk=7:
- margin +0.005917, SE 0.002233, z=2.650, n=80
- bar max(2·SE, δ=0.002) = 0.004466 (~1.325×)
- thought median 165 (≥80 ✓), B pass 0.433 (≥0.30 ✓)
- k=3, τ=0.03 (fail-closed if stamp ≠ v4)
- decision: WIN / Stage-5 licensed (r1008_sim_result_reign36_wvk7.json, p4150)
Lineage: R1000 MidCtx HiRank Midβ Mega UltraLoLR REFUTE m=−0.000317 ~−0.15× → MidCtx HiRank Midβ Mega MidLR isolate between UltraLoLR R1000 and HiLR R991; ≠ Mega UltraLoLR R1000 / ≠ Mega HiLR R991 / ≠ SoftCtx Mega UltraLoLR R959 / ≠ SoftCtx HiRank Midβ Mega MidLR R992 / ≠ Online / ≠ GRPO
Experiment path: mining/experiments/r1008-vera-offline-dpo-hialpha-hirank-midbeta-midctx-megasuperextrasteps-ep4-midlr

Intended use

SN120 Affine miner submission / evalsrv Reason v4 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 eric-the-coder/queue_merged-u103

Base model

vera6/affine-5g4yy75zuz-t6

Finetuned

(77)

this model