Instructions to use cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100 with Transformers:

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

pipe = pipeline("text-generation", model="cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100")
model = AutoModelForCausalLM.from_pretrained("cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100

SGLang

How to use cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100 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 "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100" \
    --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": "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100",
		"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 "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100" \
        --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": "cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100 with Docker Model Runner:
```
docker model run hf.co/cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100
```

Qwen2.5-7B-Instruct — Direct-OPD transfer of the R1-distillation shift, 100 steps

Artifact of the Direct-OPD SFT-transfer experiment (direct-opd-sft-transfer, condition r1distill). The question: when a pure-SFT shift encodes real held-out capability, does Direct-OPD transfer that capability — and into a student 4.7x larger than the teachers?

The training signal is the token-level shift log pi_post - log pi_pre, evaluated on the student's own sampled tokens. Neither teacher is imitated; only the difference between them is.

role	model	what it is
`pi_pre` — teacher_ref (`TEACHER_REF_MODEL_PATH`)	`Qwen/Qwen2.5-Math-1.5B` @ `4a83ca6e4526a4f2da3aa259ec36c259f66b2ab2`	the pre-shift reference: the base math model the distillation started from
`pi_post` — teacher (`REWARD_MODEL_PATH`)	`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` @ `ad9f0ae0864d7fbcd1cd905e3c6c5b069cc8b562`	the post-shift model: the same 1.5B architecture after SFT on 800K R1 traces
student init	`Qwen/Qwen2.5-7B-Instruct` @ `a09a35458c702b33eeacc393d103063234e8bc28`	non-thinking instruct model, 7.6B params

Root = step 100. checkpoint-{20,40,60,80,100}/ = intermediate merged checkpoints. Weights are bf16 (verl's FSDP->HF merge downcasts the fp32 masters).

Training configuration

code: https://github.com/BytedTsinghua-SIA/Direct-OPD @ 3a9d6bd37b00a38e7a9b2959239e4631e5324aea + logs/phase4_seed.patch (seed 42 shim)
data: cmpatino/direct-opd-sft-deepmath-pilot-data @ 22625ae5db434947195bf862c429cd94504a4809 :: opd_train.parquet (6,400 AIME-decontaminated prompts, one pass)
100 steps x 64 prompts x 4 rollouts, lr 1e-6, adaptive KL (init/max 2.5, min 0.5), token-mean loss, fp32 masters
sequence budget 768 prompt + 3328 response = 4096, pinned to pi_pre's max_position_embeddings (4096): the reward must never score a position the pre-teacher was never trained on
top-k 16 only_stu, student_p weighting, T=1.0 for student and teachers, reward_model.model.input_tokenizer=null (teachers score the student's rendered ids verbatim)
4 x A100-80GB, FSDP full-shard, gradient checkpointing + activation offload
driver + full env block: logs/run_manifest.json, console log logs/train.log.gz

Caveats

No in-training validation (test_freq=-1); all evaluation is external and pre-registered.
Bit-exact reproducibility is not attainable (vLLM continuous batching, dynamic micro-batching, FSDP reduction order). Seeding fixes data order and engine seeds only.
Prompts are rendered with the student's chat template in training and in evaluation (verl RLHFDataset, rl_dataset.py:363), so Qwen2.5's default system prompt is present in both — consistent by construction.
Special ids 151643-151649 carry different meanings in the DeepSeek post-teacher than in the Qwen pre-teacher/student; only the response's terminal <|im_end|> is affected (~1 token/response). Reported, not fixed.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for cmpatino/Qwen2.5-7B-Instruct-DirectOPD-R1DistillShift-100

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