Instructions to use thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10") 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("thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10") model = AutoModelForMultimodalLM.from_pretrained("thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10") 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 thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10
- SGLang
How to use thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 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 "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10" \ --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": "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10", "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 "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10" \ --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": "thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 with Docker Model Runner:
docker model run hf.co/thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10
Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10
This is a DGX Spark optimized quant for vLLM: a GB10-oriented compressed-tensors quantization of llmfan46/Ornith-1.0-35B-uncensored-heretic for vLLM inference.
The source model is llmfan46/Ornith-1.0-35B-uncensored-heretic.
Quantization
- Dense attention and linear-attention projections: FP8 W8A8.
- Routed MoE experts and shared expert projections: NVFP4 W4A4.
- Preserved in BF16: embeddings,
lm_head, router gates, visual modules, norms, Conv1D,A_log,dt_bias, and non-target tensors. - Visual encoder: copied from the source checkpoint and preserved in BF16 for multimodal/image support.
- Calibration data:
HuggingFaceH4/ultrachat_200k,train_sft. - Calibration samples: 512.
- Calibration sequence length: 2048.
- Pipeline: sequential, CPU offload, all MoE experts calibrated.
The quantization groups use mutually exclusive explicit target regexes.
Output
- Format:
mixed-precision. - Quantization method:
compressed-tensors. - Config groups:
group_0, group_1. - Weight layout: sharded safetensors with model.safetensors.index.json.
- Safetensors shards: 5.
- Indexed tensors: 124376.
- Max position embeddings: 262144.
- MTP weights: not present in the inspected source checkpoint.
DGX Spark vLLM
This model is intended for the GB10 patched vLLM path used by demon-zombie/Qwen3.5-122B-A10B-NVFP4-FP8Dense-GB10.
Example:
docker run --gpus all -p 8000:8000 --ipc host \
-v /opt/vllm-cache:/root/.cache/huggingface \
-e CUBLASLT_WORKSPACE_SIZE=33554432 \
-e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False \
-e RUNAI_STREAMER_MEMORY_LIMIT=4294967296 \
vllm/vllm-openai:v0.22.1 \
--model thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10 \
--served-model-name Ornith-1.0-35B-uncensored-heretic \
--kernel-config '{"moe_backend": "flashinfer_b12x"}' \
--load-format runai_streamer \
--gpu-memory-utilization 0.50 \
--kv-cache-dtype fp8 \
--enable-prefix-caching \
--enable-chunked-prefill \
--max-num-batched-tokens 4176 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_xml \
--reasoning-parser qwen3
On DGX Spark / GB10, the vLLM image may still need the same CUTLASS DSL and flashinfer SM12x patch files described in the reference model card above.
DGX Spark benchmark
Measured on NVIDIA DGX Spark / GB10 with vllm/vllm-openai:v0.22.1, the GB10 CUTLASS DSL and flashinfer SM12x patches, --gpu-memory-utilization 0.50, --kv-cache-dtype fp8, --kernel-config {"moe_backend": "flashinfer_b12x"}, and --max-num-batched-tokens 4176.
- vLLM log throughput during a warm single request:
Avg generation throughput: 60.8 tokens/s. - Client-side measurement for the same warm request: 1024 completion tokens in 16.905s, about
60.58 tok/s. - First measured request after server readiness: 768 completion tokens in 13.676s, about
56.15 tok/s; vLLM log showedAvg generation throughput: 53.5 tokens/s. - Model load memory reported by vLLM: 21.04 GiB.
- Max model length reported by vLLM: 262144 tokens.
- GPU KV cache size at
--gpu-memory-utilization 0.50: 3,344,321 tokens. - Maximum concurrency reported by vLLM for 262144-token requests: 12.76x.
These figures are from one local DGX Spark run and may change with prompt shape, sampling settings, vLLM version, patch versions, and CUDA graph/cache warmup state.
Status
Local DGX Spark conversion validation:
- Source:
llmfan46/Ornith-1.0-35B-uncensored-heretic. - Conversion completed with the same NVFP4-FP8Dense GB10 recipe used for the non-uncensored Ornith 35B build.
verify-output.pypassed after vLLM config patching and visual tensor merge.- Visual tensors preserved: 333
model.visual.*tensors copied from the source checkpoint. - vLLM serving smoke test passed on DGX Spark / GB10 after upload.
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Model tree for thanet-s/Ornith-1.0-35B-uncensored-heretic-nvfp4-fp8dense-gb10
Base model
deepreinforce-ai/Ornith-1.0-35B