Instructions to use ressl/Ornith-1.5-397B-uncensored-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ressl/Ornith-1.5-397B-uncensored-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="ressl/Ornith-1.5-397B-uncensored-FP8")
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("ressl/Ornith-1.5-397B-uncensored-FP8")
model = AutoModelForMultimodalLM.from_pretrained("ressl/Ornith-1.5-397B-uncensored-FP8", 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 ressl/Ornith-1.5-397B-uncensored-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ressl/Ornith-1.5-397B-uncensored-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/Ornith-1.5-397B-uncensored-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ressl/Ornith-1.5-397B-uncensored-FP8

SGLang

How to use ressl/Ornith-1.5-397B-uncensored-FP8 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 "ressl/Ornith-1.5-397B-uncensored-FP8" \
    --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": "ressl/Ornith-1.5-397B-uncensored-FP8",
		"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 "ressl/Ornith-1.5-397B-uncensored-FP8" \
        --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": "ressl/Ornith-1.5-397B-uncensored-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ressl/Ornith-1.5-397B-uncensored-FP8 with Docker Model Runner:
```
docker model run hf.co/ressl/Ornith-1.5-397B-uncensored-FP8
```

Ornith-1.5-397B uncensored (FP8)

TL;DR: Ornith-1.5-397B with refusal behavior ablated from the weights in two norm-preserving biprojection passes (1/1120 hard refusals across five independent harmful-prompt datasets), as a drop-in FP8 replacement for ornith-ai/Ornith-1.5-397B-FP8.

This is the 397B flagship of the Ornith-1.5 family (Qwen3.5-MoE, 60 layers, 512 experts, hybrid linear/full attention, 262k context, multimodal). The first ablation pass removed the classic "I cannot" refusal style; a second pass targeted the residual hedged-decline circuit ("I'd rather not") using directions computed from refused-vs-complied residuals of the same harmful prompt distribution. Intended for security research, red-teaming and creative work where stock refusals get in the way. Use responsibly.

Facts & figures


Base model	ornith-ai/Ornith-1.5-397B
Method	2x norm-preserving biprojection abliteration (grimjim/TrevorS method) on the BF16 master, then channel-wise FP8 (compressed-tensors, per-output-channel symmetric scales, dynamic activations)
Size	405 GB (from 794 GB BF16)
Hard refusals	1/1120 cross-dataset, 0/100 mlabonne (details below)
GPQA-Diamond	0.631 (198 questions, temp 0)
Needle (long context)	9/9 passcodes recalled at 32k / 64k / 128k
Tool calling	2/2 correct tool_calls via qwen3_coder parser
KL vs base	mean 0.66 / median 0.47 at the answer-start position (36 held-out prompts; harmless-only subset mean 0.33; the harmful subset carries the intended behavior change)
Context length	262,144 tokens
Quant hardware	7x NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120) for abliteration and export
Validated serving	SGLang, tensor-parallel 2 x pipeline-parallel 3 on 6x RTX PRO 6000
Toolchain	transformers 5.12.1, custom streaming export, SGLang (sglang-omega build)

Refusal evaluation (the honest version)

Served FP8 artifact, temperature 0. Scored with an extended hard-decline list that covers both the classic style ("I cannot", "I won't", ...) and hedged declines ("I'd rather not", "I'll pass", "I'd hesitate", ...). A naive keyword scorer that only knows the classic style undercounts refusals on this model; every flagged response was manually reviewed and is a true refusal.

Dataset	Hard refusals
mlabonne harmful behaviors (100)	0/100
JailbreakBench JBB-Behaviors (100)	0/100
tulu-3-harmbench-eval (320)	0/320
HarmfulQA (300)	0/300
LLM-LAT harmful (300)	1/300
Total	1/1120

Baseline comparison on mlabonne (same scorer): the stock model scores 91/100 (measured on a locally requantized FP8 of the official BF16; the official FP8 repo itself was not evaluated).

Benchmark comparison (custom zero-shot protocol, temp 0, same FP8 format both sides)

Suite	baseline	this model
MMLU-Pro (500, test split, seed 42)	0.810	0.814
GSM8K (200)	0.950	0.950
GPQA-Diamond (198)	not run under this protocol	0.631

MMLU/GSM8K pairs: paired per-question analysis shows no measurable difference (MMLU delta +0.4 pp, 95% bootstrap CI [-1.8, +2.6] pp; GSM8K delta 0). MMLU answers without a parseable "Answer: X" counted as wrong on both sides (57/500 vs 55/500). The baseline was measured on the v1 ablated build's sibling requant of the original BF16; the GPQA number above is this v2 artifact (v1 measured 0.647 on the same set).

Run it with SGLang

Validated command (TP=2 x PP=3 on 6x 96 GB GPUs; plain TP=7 is impossible because 32 attention heads and 2 KV heads are not divisible by 7, TP=4 is too small for 405 GB):

python -m sglang.launch_server \
    --model-path ressl/Ornith-1.5-397B-uncensored-FP8 \
    --served-model-name Ornith-1.5-397B-uncensored-FP8 \
    --host 0.0.0.0 --port 30000 \
    --tp-size 2 --pp-size 3 \
    --trust-remote-code --dtype auto \
    --context-length 262144 \
    --kv-cache-dtype fp8_e4m3 \
    --max-running-requests 8 \
    --chunked-prefill-size 16384 \
    --mem-fraction-static 0.88 \
    --tool-call-parser qwen3_coder \
    --reasoning-parser qwen3

Companion formats

NVFP4 (232 GB, serves on 4x RTX PRO 6000): ressl/Ornith-1.5-397B-uncensored-NVFP4

Quality & limitations

Will comply with requests a stock model refuses. This is the point; use responsibly and within your local laws.
Reasoning model: answers open with a <think> block unless disabled via the chat template (the repo ships the stock template; sglang's qwen3 reasoning parser splits it).
Identity answers (who are you) reflect the base model's training (it may name a vendor it was distilled from). That is inherited from ornith-ai/Ornith-1.5-397B, not an artifact of the abliteration.
The second ablation pass raises drift vs base (harmless-subset KL 0.33 vs 0.16 after pass 1) while keeping GPQA/needle/coherence intact; that trade is visible in the numbers above rather than hidden.
Eval coverage: refusal sets (1,120 prompts), GPQA/MMLU-Pro/GSM8K subsets, needle, tool smoke. No full benchmark suite (Terminal-Bench, SWE-bench) was run.

Provenance & reproducibility

Pass 1: per-layer refusal directions from 800 harmful/harmless prompts (mlabonne harmful_behaviors + harmless_alpaca, residuals at the answer-start position, orthogonalized against the harmless mean, winsorized 0.995). Pass 2: directions from 103 prompts the pass-1 model still refused vs 500 it complied with (paraphrase augmented harvest). Both passes apply norm-preserving biprojection to all residual-writing matrices across all 60 layers (self_attn.o_proj, linear_attn.out_proj, shared expert down_proj, all 512 routed expert down_proj matrices per layer, 180 tensors). FP8 export reproduces the official repo structure tensor-for-tensor (per-expert layout, bf16 scales, identical quantization_config); scale math and expert split verified against the official FP8 checkpoint before export.

License & credits

MIT license, inherited from the base model by the Ornith AI authors. Abliteration, quantization and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon).

Support this work: if these models are useful to you, consider supporting on Patreon; more at ressl.ch.

Version note (2026-08-23): v2 artifact. v1 (single-pass abliteration) scored 6/100 mlabonne / 39/1120 cross-dataset; v2 scores 0/100 / 1/1120 on the same extended scorer.

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Safetensors

Model size

397B params

Tensor type

BF16

F8_E4M3

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