arcee-ai
/

AFM-4.5B-Base-KDA-NoPE

@@ -1,199 +1,129 @@
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 ---
+license: apache-2.0
+language:
+  - en
 library_name: transformers
+base_model:
+  - arcee-ai/AFM-4.5B-Base
+tags:
+  - kda
+  - kimi-delta-attention
+  - linear-attention
+  - nope
+  - hybrid-attention
+  - distillation
+  - research
 ---
+# AFM-4.5B-Base-KDA-NoPE
+A hybrid attention variant of [AFM-4.5B-Base](https://huggingface.co/arcee-ai/AFM-4.5B-Base) combining Kimi Delta Attention (KDA) with NoPE (No Positional Encoding) full-attention layers in a 3:1 ratio. This architecture balances efficiency with performance through knowledge distillation.
+> ⚠️ **Research Model**: This is an experimental model released for research purposes. For production use, see [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B).
+More details available in our blog post here: https://www.arcee.ai/blog/distilling-kimi-delta-attention-into-afm-4-5b-and-the-tool-we-used-to-do-it
+## Overview
+Following the Kimi Linear architecture pattern, this model interleaves KDA layers with periodic full-attention layers (using NoPE) in a 3:1 ratio. This hybrid structure reduces memory and KV-cache usage while preserving global information flow via the full attention layers.
+**Key characteristics:**
+- 3:1 KDA to full-attention ratio
+- Full attention layers use NoPE (No Positional Encoding)
+- Trained up to 32k sequence length
+- Better short-context performance than pure KDA
+- Reduced memory footprint compared to full attention
+## Architecture
+| Component | Details |
+|-----------|---------|
+| Parameters | 4.5B |
+| Attention Pattern | 1 Full Attn (NoPE) : 3 KDA |
+| Positional Encoding | NoPE on full attention layers |
+| Max Training Length | 32k tokens |
+| Base Model | AFM-4.5B-Base |
+## Benchmark Results
+Performance compared to the teacher model and other configurations:
+| Benchmark | Teacher (Full Attn) | Hybrid (KDA-NoPE) | KDA-Only |
+|-----------|:-------------------:|:-----------------:|:--------:|
+| MMLU (Avg) | 63.1% | 55.1% | 55.8% |
+| ARC-Challenge | 55.6% | 48.5% | 49.9% |
+| HellaSwag (Norm) | 78.0% | 74.3% | 74.3% |
+| GSM8K (Math) | 52.1% | 36.5% | 26.8% |
+### Key Findings
+- **Math advantage**: The hybrid recovers significantly more math performance (36.5%) than pure KDA (26.8%)
+- **Knowledge benchmarks**: Performs comparably to KDA-Only on MMLU, ARC, and HellaSwag
+- **Efficiency**: Maintains efficiency gains from KDA while preserving global reasoning via NoPE layers
+## Long-Context Performance (NIAH)
+The hybrid model shows distinct long-context behavior:
+- 100% single-needle retrieval up to 32k
+- Sharp performance cliff past 32k training length
+- Near-zero performance beyond training context (vs. smooth degradation for KDA-Only)
+The NoPE full-attention layers appear responsible for the hard cutoff—they haven't seen positions beyond 32k during training. KDA layers generalize more naturally to longer sequences.
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "arcee-ai/AFM-4.5B-Base-KDA-NoPE"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+prompt = "The theory of relativity states that"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## Training Details
+- **Method**: Knowledge distillation from AFM-4.5B-Base using [DistillKit](https://github.com/arcee-ai/DistillKit)
+- **Teacher**: AFM-4.5B-Base (full attention)
+- **Student Architecture**: Hybrid 3:1 KDA:NoPE
+- **Training Length**: 32k sequence length
+## Comparison: Hybrid vs KDA-Only
+| Aspect | Hybrid (KDA-NoPE) | KDA-Only |
+|--------|:-----------------:|:--------:|
+| Math (GSM8K) | 36.5% ✓ | 26.8% |
+| Within-training NIAH | 100% | 100% |
+| Beyond-training behavior | Hard cliff | Smooth degradation |
+| Memory efficiency | ~75% reduction | ~100% reduction |
+Choose **Hybrid** for better short-context reasoning, especially math. Choose **KDA-Only** for more predictable long-context degradation.
+## Intended Use
+This model is intended for:
+- Research into hybrid attention architectures
+- Studying linear/full attention tradeoffs
+- Exploring NoPE attention in hybrid configurations
+- Benchmarking efficiency vs. capability tradeoffs
+## License
+AFM-4.5B is released under the Apache-2.0 license.