Instructions to use Ammonix/AmmonixRCM-Writer-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ammonix/AmmonixRCM-Writer-9B with Transformers:

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

pipe = pipeline("text-generation", model="Ammonix/AmmonixRCM-Writer-9B")
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("Ammonix/AmmonixRCM-Writer-9B")
model = AutoModelForMultimodalLM.from_pretrained("Ammonix/AmmonixRCM-Writer-9B", 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 Ammonix/AmmonixRCM-Writer-9B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Ammonix/AmmonixRCM-Writer-9B

SGLang

How to use Ammonix/AmmonixRCM-Writer-9B 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 "Ammonix/AmmonixRCM-Writer-9B" \
    --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": "Ammonix/AmmonixRCM-Writer-9B",
		"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 "Ammonix/AmmonixRCM-Writer-9B" \
        --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": "Ammonix/AmmonixRCM-Writer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ammonix/AmmonixRCM-Writer-9B with Docker Model Runner:
```
docker model run hf.co/Ammonix/AmmonixRCM-Writer-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AmmonixRCM-Writer-9B

The frozen local language model of the Ammonix RCM Agent: it writes the claim paperwork after each decision. The decision path makes no language-model call — this model only writes, it never decides.

This is the exact artifact evaluated in the RCM paper (https://doi.org/10.5281/zenodo.22871212), a LoRA (r=32) trained on 1,339 self-generated pairs rewarded by the payer's verdict — no teacher model — merged into the base weights. Under the paper's payer, this trained writer loses the least money of all four writers tested (the writer comparison table).

Evaluation

Four writers were swapped into the same seat under identical decisions (200 demonstration claims, 525 decision points; the payer's reviewer reads every appeal letter against the records it holds — a letter with an unsupported statement comes back unprocessed):

Writer	Rejected by payer	Money on rejected claims
Qwen 27B	48	$27,039
Ornith 1.5 9B (base)	44	$23,933
This model	16	$7,709
Claude Opus 5	37	$21,157

Decisions were identical under all four writers (525/525) — the agent decides from recorded outcomes; the writer only affects whether the paperwork survives the payer's reading.

Training

Data: fully synthetic. Every training pair comes from the Cardessa synthetic claims world; no real patient, provider, or payer data was used anywhere in training or evaluation.
Rejection-sampling SFT with no teacher model: the base 9B drafts each claim's paperwork 8× at temperature 0.8; each draft is scored by the world's own payer, and the best draft per claim becomes a training pair (1,503 claims → 1,339 pairs).
LoRA r=32, 2 epochs, ~3 h on one consumer GPU, then merged into the base weights. The pairs and training script ship in the code repository.

Limitations and out-of-scope use

Trained and evaluated only on the synthetic world; it has never seen a real claim, real payer rules, or real denial codes. Its writing style is fitted to one simulated payer's reviewer.
It is a writer, not a decision-maker: outside the agent's harness (schema-constrained decoding, scrubber, escalation rules) its outputs are unchecked.
Not for production billing, clinical use, or any submission to a real payer.

License

Released under the Ammonix Research License (LICENSE.md): research, educational, and evaluation use is free — including evaluation by a commercial organization deciding whether to seek a commercial license. Any commercial use requires a separate license from Ammonix — licensing@ammonix.ai.

Verify your download

Every shard's SHA-256 is pinned in the code repository (runs/manifests/writer_pin.json) — your download should hash identically.

Use

Serve with vLLM (the agent's configuration: temperature 0, seed 0, JSON-schema-constrained decoding, thinking disabled) and point the RCM agent's writer at it — see the code repo's REPRODUCING.md. The adapter/ folder carries the un-merged LoRA for use on the pinned base (ornith-ai/Ornith-1.5-9B @ c927ad73b7eb); training pairs and scripts ship in the code repository, so the adapter can also be retrained from scratch.

Disclaimer: research demonstration on a fully synthetic claims world — not a medical device, not for clinical or production billing use.

Cite

See the code repository's CITATION.cff (paper: https://doi.org/10.5281/zenodo.22871212). Commercial licensing: licensing@ammonix.ai

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for Ammonix/AmmonixRCM-Writer-9B

Base model

ornith-ai/Ornith-1.5-9B

Finetuned

(34)

this model

Quantizations

1 model