Update README.md

README.md

@@ -18,3 +18,75 @@ tags:
 - mlx
 pipeline_tag: text-generation
 ---
+
+<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 Nano MLX 8bit
+
+Trinity Nano Preview is a preview of Arcee AI's 6B MoE model with 1B active parameters. It is the small-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
+
+This is a chat tuned model, with a delightful personality and charm we think users will love. We note that this model is pushing the limits of sparsity in small language models with only 800M non-embedding parameters active per token, and as such **may be unstable** in certain use cases, especially in this preview.
+
+This is an *experimental* release, it's fun to talk to but will not be hosted anywhere, so download it and try it out yourself!
+
+***
+
+Trinity Nano Preview 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:** 6B, 1B 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)
+
+## Use with mlx
+
+```
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler, make_logits_processors
+
+model, tokenizer = load("arcee-ai/Trinity-Nano-Preview-MLX-8bit")
+
+prompt = "What is the capital of France?"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+sampler = make_sampler(temp=0.1, top_k=50, top_p=0.1)
+logits_processors = make_logits_processors(repetition_penalty=1.05)
+
+response = generate(
+    model,
+    tokenizer,
+    prompt=prompt,
+    max_tokens=512,
+    sampler=sampler,
+    logits_processors=logits_processors,
+    verbose=True,
+)
+```
+