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

Libraries

How to use willamazon1/mopd-iter200 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willamazon1/mopd-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-iter200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

mopd-iter200

Multi-teacher On-Policy Distillation (OPD) checkpoint of a Qwen3-8B student, exported at iteration 200.

Overview

Architecture: Qwen3-8B (dense, 36 layers, hidden 4096, GQA 32Q/8KV, vocab 151936, 32k context)
Training: on-policy distillation where a single student rolls out a mixed math + search + tool-use (tau) stream; the only training signal is per-token reverse-KL to a domain-specific teacher (static domain routing, task reward = 0).
Init: Qwen3-8B SFT chain (Math -> Sea-SFT -> Search -> Tau-SFT -> IF).
Format: converted from a Megatron torch_dist checkpoint to HuggingFace safetensors (bf16).

Usage

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

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

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

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for willamazon1/mopd-iter200

Base model

Qwen/Qwen3-8B-Base

Finetuned

(480)

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