Instructions to use wAI-org/tmax-9b-capped-dppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wAI-org/tmax-9b-capped-dppo with Transformers:

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

pipe = pipeline("text-generation", model="wAI-org/tmax-9b-capped-dppo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wAI-org/tmax-9b-capped-dppo")
model = AutoModelForCausalLM.from_pretrained("wAI-org/tmax-9b-capped-dppo", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wAI-org/tmax-9b-capped-dppo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wAI-org/tmax-9b-capped-dppo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wAI-org/tmax-9b-capped-dppo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wAI-org/tmax-9b-capped-dppo

SGLang

How to use wAI-org/tmax-9b-capped-dppo 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 "wAI-org/tmax-9b-capped-dppo" \
    --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": "wAI-org/tmax-9b-capped-dppo",
		"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 "wAI-org/tmax-9b-capped-dppo" \
        --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": "wAI-org/tmax-9b-capped-dppo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wAI-org/tmax-9b-capped-dppo with Docker Model Runner:
```
docker model run hf.co/wAI-org/tmax-9b-capped-dppo
```

tmax-9b-capped-dppo / README.md

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step1000: original checkpoint artifacts (safetensors sha256 9fd1feb4fb551978)

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---
license: apache-2.0
base_model: hamishivi/Qwen3.5-9B
tags: [tmax, rl, checkpoint, research]
library_name: transformers
---
# tmax-9b-capped-dppo — optimizer step 1000

Research checkpoint from the TMax RL study. **capped DPPO** arm at **optimizer step 1000**.

Each optimizer-step milestone is a separate **branch** of this repository:

```python
from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained("wAI-org/tmax-9b-capped-dppo", revision="step1000")
```

## Identity and provenance

| field | value |
|---|---|
| stable run ID | `oscar-cap500-dppo-sandfleet__seed42__1788470999` |
| arm label | capped DPPO |
| optimizer step | 1000 |
| optimizer configuration | capped DPPO, continued to 1000 optimizer steps |
| base model | [hamishivi/Qwen3.5-9B](https://huggingface.co/hamishivi/Qwen3.5-9B) |
| `model.safetensors` sha256 | `9fd1feb4fb5519781e02d6164dde0d5a8cc8d52378b46c584d54f381bf231256` |

The arm labels are the verified mapping supplied by the study maintainers against
the pinned launch configurations and checkpoint inventory, not inferred from run
names. Steps are the trainer's own `step_N` HF exports, i.e. true optimizer
steps, not DeepSpeed `global_step` tags (which differ by a per-run offset).

## Files

The six files here are the checkpoint's original artifacts, unmodified. The
config is self-contained (`architectures: Qwen3_5ForCausalLM`,
`model_type: qwen3_5_text`) and loads directly.

**Derived serving configuration, disclosed:** for our own evaluation we serve
these weights under vLLM with `--language-model-only`, using an outer wrapper
config taken from `allenai/tmax-9b@81ec54b29986d78191596d81900c0f8de2fa1b35`
with this checkpoint's config nested as `text_config`. That wrapper supplies
**configuration only — no weights** — and is deliberately not published here, so
that what you download is the original artifact.

## Scope and limitations

- These are **research checkpoints**, released for inference.
- Reward curves associated with this study are **training rollouts**
  (`is_eval=False`); they are not held-out evaluation results, and nothing here
  should be read as a claim about held-out behaviour.
- "Reset-to-eval control" is coordination shorthand used during the study. It
  does not denote a particular weight-reset algorithm.

	---
	license: apache-2.0
	base_model: hamishivi/Qwen3.5-9B
	tags: [tmax, rl, checkpoint, research]
	library_name: transformers
	---

	# tmax-9b-capped-dppo — optimizer step 1000

	Research checkpoint from the TMax RL study. capped DPPO arm at optimizer step 1000.

	Each optimizer-step milestone is a separate branch of this repository:

	```python
	from transformers import AutoModelForCausalLM
	m = AutoModelForCausalLM.from_pretrained("wAI-org/tmax-9b-capped-dppo", revision="step1000")
	```

	## Identity and provenance

	\| field \| value \|
	\|---\|---\|
	\| stable run ID \| `oscar-cap500-dppo-sandfleet__seed42__1788470999` \|
	\| arm label \| capped DPPO \|
	\| optimizer step \| 1000 \|
	\| optimizer configuration \| capped DPPO, continued to 1000 optimizer steps \|
	\| base model \| [hamishivi/Qwen3.5-9B](https://huggingface.co/hamishivi/Qwen3.5-9B) \|
	\| `model.safetensors` sha256 \| `9fd1feb4fb5519781e02d6164dde0d5a8cc8d52378b46c584d54f381bf231256` \|

	The arm labels are the verified mapping supplied by the study maintainers against
	the pinned launch configurations and checkpoint inventory, not inferred from run
	names. Steps are the trainer's own `step_N` HF exports, i.e. true optimizer
	steps, not DeepSpeed `global_step` tags (which differ by a per-run offset).

	## Files

	The six files here are the checkpoint's original artifacts, unmodified. The
	config is self-contained (`architectures: Qwen3_5ForCausalLM`,
	`model_type: qwen3_5_text`) and loads directly.

	Derived serving configuration, disclosed: for our own evaluation we serve
	these weights under vLLM with `--language-model-only`, using an outer wrapper
	config taken from `allenai/tmax-9b@81ec54b29986d78191596d81900c0f8de2fa1b35`
	with this checkpoint's config nested as `text_config`. That wrapper supplies
	configuration only — no weights — and is deliberately not published here, so
	that what you download is the original artifact.

	## Scope and limitations

	- These are research checkpoints, released for inference.
	- Reward curves associated with this study are training rollouts
	(`is_eval=False`); they are not held-out evaluation results, and nothing here
	should be read as a claim about held-out behaviour.
	- "Reset-to-eval control" is coordination shorthand used during the study. It
	does not denote a particular weight-reset algorithm.