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
+---
+<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 FP8-Block
+
+**This repository contains the FP8 block-quantized weights of Trinity-Mini (FP8 weights and activations with per-block scaling).**
+
+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)
+
+Try it out now at [chat.arcee.ai](http://chat.arcee.ai/)
+
+***
+
+## 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
+
+***
+
+## Quantization Details
+
+- **Scheme:** `FP8 Block` (FP8 weights and activations, per-block scaling with E8M0 scale format)
+- **Format:** `compressed-tensors`
+- **Intended use:** High-throughput FP8 deployment of Trinity-Mini with near-lossless quality, optimized for NVIDIA Hopper GPUs
+- **Supported backends:** [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM), vLLM CUTLASS, Triton
+
+## 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>
+
+### Running our model
+
+- [VLLM](https://huggingface.co/arcee-ai/Trinity-Mini-FP8-Block#vllm)
+- [Transformers](https://huggingface.co/arcee-ai/Trinity-Mini-FP8-Block#transformers)
+
+## VLLM
+
+Supported in VLLM release 0.18.0+ with DeepGEMM FP8 MoE acceleration.
+
+```
+# pip
+pip install "vllm>=0.18.0"
+```
+
+Serving the model with DeepGEMM enabled:
+
+```
+VLLM_USE_DEEP_GEMM=1 vllm serve arcee-ai/Trinity-Mini-FP8-Block \
+  --trust-remote-code \
+  --max-model-len 4096 \
+  --enable-auto-tool-choice \
+  --reasoning-parser deepseek_r1 \
+  --tool-call-parser hermes
+```
+
+Serving without DeepGEMM (falls back to CUTLASS/Triton):
+
+```
+vllm serve arcee-ai/Trinity-Mini-FP8-Block \
+  --trust-remote-code \
+  --max-model-len 4096 \
+  --enable-auto-tool-choice \
+  --reasoning-parser deepseek_r1 \
+  --tool-call-parser hermes
+```
+
+## Transformers
+
+Use the `main` transformers branch
+
+```
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+
+# pip
+pip install '.[torch]'
+
+# uv
+uv pip install '.[torch]'
+```
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "arcee-ai/Trinity-Mini-FP8-Block"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.15,
+    top_k=50,
+    top_p=0.75,
+    min_p=0.06
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## API
+
+Trinity Mini is available today on openrouter:
+
+https://openrouter.ai/arcee-ai/trinity-mini
+
+```
+curl -X POST "https://openrouter.ai/v1/chat/completions" \
+  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "arcee-ai/trinity-mini",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What are some fun things to do in New York?"
+      }
+    ]
+  }'
+```
+
+## License
+
+Trinity-Mini-FP8-Block is released under the Apache-2.0 license.