Instructions to use willamazon1/mopd-sdft-iter200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willamazon1/mopd-sdft-iter200 with Transformers:

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

pipe = pipeline("text-generation", model="willamazon1/mopd-sdft-iter200")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willamazon1/mopd-sdft-iter200")
model = AutoModelForCausalLM.from_pretrained("willamazon1/mopd-sdft-iter200", 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 willamazon1/mopd-sdft-iter200 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willamazon1/mopd-sdft-iter200"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willamazon1/mopd-sdft-iter200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/willamazon1/mopd-sdft-iter200

SGLang

How to use willamazon1/mopd-sdft-iter200 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 "willamazon1/mopd-sdft-iter200" \
    --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": "willamazon1/mopd-sdft-iter200",
		"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 "willamazon1/mopd-sdft-iter200" \
        --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": "willamazon1/mopd-sdft-iter200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use willamazon1/mopd-sdft-iter200 with Docker Model Runner:
```
docker model run hf.co/willamazon1/mopd-sdft-iter200
```

mopd-sdft-iter200 / README.md

willamazon1

Add mopd-sdft-iter200 (multi-teacher OPD, light-SFT student init, iter 200)

f876076 verified 12 days ago

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3-8B-Base
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- on-policy-distillation
	- multi-teacher
	- math
	- search
	- tool-use
	---

	# mopd-sdft-iter200

	A Qwen3-8B model trained with multi-teacher On-Policy Distillation (OPD) over a mixed
	math + search + tool-use (tau) stream.

	## Training

	- Architecture: Qwen3-8B (36 layers, hidden 4096, FFN 12288, 32 query / 8 KV heads,
	head_dim 128, vocab 151936, QK-layernorm, untied embeddings, RoPE θ=1e6).
	- Student initialization: a light supervised-finetuned base
	(`Qwen3-8B-oracle-mix-SFT`, balanced oracle-mix, iteration 500) derived from
	`Qwen/Qwen3-8B-Base`. This light-SFT start gives the student the tool-call /
	instruction-following priors needed to emit trainable agentic trajectories before OPD.
	- Method: On-policy distillation. A single student rolls out a mixed
	math + search + tau trajectory stream; a static per-sample domain tag routes each
	trajectory to a domain-specific teacher (math / search / tau). The only training
	signal is the per-token reverse-KL from the student to its domain teacher
	(task reward = 0).
	- Checkpoint: iteration 200.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "willamazon1/mopd-sdft-iter200"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

	msgs = [{"role": "user", "content": "What is 12*8?"}]
	text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
	ids = tok(text, return_tensors="pt").input_ids.to(model.device)
	out = model.generate(ids, max_new_tokens=64, do_sample=False)
	print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
	```

	## Notes

	- Weights are the converted HuggingFace `safetensors` export (bf16, 399 tensors,
	verified free of NaN/Inf) of a Megatron `torch_dist` training checkpoint.
	- Tokenizer and config are inherited from the Qwen3-8B lineage.