Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+- es
+- fr
+- de
+- it
+- pt
+- ru
+- ar
+- hi
+- ko
+- zh
+library_name: transformers
+base_model:
+- arcee-ai/Trinity-Mini
+base_model_relation: quantized
+tags:
+  - moe
+  - nvfp4
+  - modelopt
+  - blackwell
+  - vllm
+---
+<div align="center">
+  <picture>
+    <img
+      src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
+      alt="Arcee Trinity Mini"
+      style="max-width: 100%; height: auto;"
+    >
+  </picture>
+</div>
+
+# Trinity Mini NVFP4
+
+**This repository contains the NVFP4 quantized weights of Trinity-Mini for deployment on NVIDIA Blackwell GPUs.**
+
+Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
+
+This model is tuned for reasoning, but in testing, it uses a similar total token count to competitive instruction-tuned models.
+
+***
+
+Trinity Mini is trained on 10T tokens gathered and curated through a key partnership with [Datology](https://www.datologyai.com/), building upon the excellent dataset we used on [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) with additional math and code.
+
+Training was performed on a cluster of 512 H200 GPUs powered by [Prime Intellect](https://www.primeintellect.ai/) using HSDP parallelism.
+
+More details, including key architecture decisions, can be found on our blog [here](https://www.arcee.ai/blog/the-trinity-manifesto)
+
+***
+
+## Model Details
+
+* **Model Architecture:** AfmoeForCausalLM
+* **Parameters:** 26B, 3B active
+* **Experts:** 128 total, 8 active, 1 shared
+* **Context length:** 128k
+* **Training Tokens:** 10T
+* **License:** [Apache 2.0](https://huggingface.co/arcee-ai/Trinity-Mini#license)
+* **Recommended settings:**
+    * temperature: 0.15
+    * top_k: 50
+    * top_p: 0.75
+    * min_p: 0.06
+
+***
+
+## Benchmarks
+
+![](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/UMV0OZh_H1JfvgzBTXh6u.png)
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
+  </picture>
+</div>
+
+## Quantization Details
+
+- **Scheme:** NVFP4 (`nvfp4_mlp_only` — MLP/expert weights only, attention remains BF16)
+- **Tool:** [NVIDIA ModelOpt](https://github.com/NVIDIA/Model-Optimizer)
+- **Calibration:** 512 samples, seq_length=2048, all-expert calibration enabled
+- **KV cache:** Not quantized
+
+## Running with vLLM
+
+Requires [vLLM](https://github.com/vllm-project/vllm) >= 0.18.0. Native FP4 compute requires Blackwell GPUs; older GPUs fall back to Marlin weight decompression automatically.
+
+### Blackwell GPUs (B200/B300/GB300) — Docker (recommended)
+
+```bash
+docker run --runtime nvidia --gpus all -p 8000:8000 \
+  -v ~/.cache/huggingface:/root/.cache/huggingface \
+  vllm/vllm-openai:v0.18.0-cu130 \
+  arcee-ai/Trinity-Mini-NVFP4 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.90 \
+  --max-model-len 8192
+```
+
+### Hopper GPUs (H100/H200) and others
+
+```bash
+vllm serve arcee-ai/Trinity-Mini-NVFP4 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.90 \
+  --max-model-len 8192 \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+ **Note (Blackwell pip installs):** If installing vLLM via pip on Blackwell rather than using Docker, native FP4 kernels may produce incorrect output due to package version mismatches. As a workaround, force the Marlin backend:
+
+ ```bash
+ export VLLM_NVFP4_GEMM_BACKEND=marlin
+
+ vllm serve arcee-ai/Trinity-Mini-NVFP4 \
+   --trust-remote-code \
+   --moe-backend marlin \
+   --gpu-memory-utilization 0.90 \
+   --max-model-len 8192 \
+   --host 0.0.0.0 \
+   --port 8000
+ ```
+
+Marlin decompresses FP4 weights to BF16 for compute, providing the full memory compression benefit (~3.7× vs BF16) but not native FP4 compute speedup. On Hopper GPUs (H100/H200), Marlin is selected automatically and no extra flags are needed.
+
+## License
+
+Trinity-Mini-NVFP4 is released under the Apache-2.0 license.