Myosotis-1-base (100M)

Myosotis-1-base is the first flagship release from us, introducing a 100-million parameter recurrent language model built on the FWKV architecture.

Myosotis-1 is engineered to never truly forget—using a mathematically clamped exponential decay that guarantees an infinite effective context window while maintaining blazing-fast inference on consumer hardware.

Architecture at a Glance

Myosotis-1 occupies a unique position in the sequence modeling spectrum: it acts as a pristine mathematical bridge between Linear State Space Models (S4/S5) and the WKV (RWKV) gating paradigm.

Component	Specification
Type	Diagonal LTI (Linear Time-Invariant) SSM + RWKV-style Gating
Total Parameters	~100 Million
Hidden Dimension (`d_model`)	768
Embedding Bottleneck (`d_emb`)	192
Layers (`n_layers`)	13
FFN Expansion Factor	4× (GELU activation)
Context Length	1024 tokens (packed training)
Vocabulary	50,257 (GPT-2 tokenizer
Weight Tying	Fully tied, factorized input/output head

Core Technical Innovations

1. The FWKV Recurrent Core

Instead of pairwise attention, Myosotis uses a fixed-size state vector updated via a gated linear recurrence:

$S_t = S_{t-1} \odot W + k_t \odot v_t$

$W = \text{clamp}(\sigma(w), 0.1)$ is a learned, constant-per-channel decay.
By clamping the minimum decay to 0.1, the model guarantees that past information decays exponentially but never reaches zero.

2. Training via $O(\log T)$ Parallel Scan

Because $W$ is data-independent (LTI), the recurrence can be trained using a pure-PyTorch Hillis-Steele associative scan. This bypasses the sequential bottleneck of RNNs without requiring custom CUDA kernels, making the architecture trivially portable across NVIDIA, AMD, Apple Silicon, and standard CPUs.

3. Factorized Tied Embeddings

We employ a low-rank factorization for the embedding layer:

Stored weight shape: $V \times d_{emb}$ (vs. standard $V \times d_{model}$ ), with a learned linear projection mapping inputs up to the full hidden dimension $d_{model}$ before the first block, and back down to $d_{emb}$ for the final logits (so the internal representation remains $d_{model}$ ).
This saves $V \times (d_{model} - d_{emb})$ parameters (~15 million in our setup) while maintaining full representational capacity.
The output head uses the transposed projection, enabling chunked cross-entropy loss to avoid materializing huge logit tensors during training.

4. Loss Masking for Conversational AI

Fine-tuned exclusively on multi-turn chat data, the loss is masked so that only assistant tokens contribute to the gradient. This forces the model to master response generation while treating user prompts purely as contextual memory.

Training Regimen

Myosotis-1 was trained in two distinct stages, leveraging the Chinchilla scaling law (20× parameter count in tokens) for optimal performance.

Stage 1: Pretraining (General Language)

Data Mixture (SmolLM-Corpus style):
- 60% fineweb-edu-dedup (educational web text)
- 25% cosmopedia-v2 (synthetic textbooks)
- 15% python-edu (code understanding)
Total Tokens: ~2 Billion (Chinchilla-optimal)
Optimizer: AdamW (fused), $\text{LR} = 1 \times 10^{-4}$
Precision: BF16 Mixed Precision with Gradient Checkpointing

Val perplexity at save time: 62.76

Performance & Speed

Myosotis-1 is optimized for hardware portability over pure theoretical FLOPs. It achieves this by avoiding data-dependent (selective) gating, allowing pure vectorized operations.

Inference Style: True RNN. No growing KV cache. Constant memory usage $L \times d_{model}$ .
CPU Speed : ~[...] tokens/second (pure PyTorch, no custom kernels).
GPU Speed : ~130 tokens/second with BF16.

While we sacrifice the "selectivity" of models like Mamba, we gain the ability to run effectively on devices where CUDA is unavailable (edge devices, free-tier cloud notebooks, and standard servers).

Evaluation

Tasks	Version	Filter	Metric		Value		Stderr
arc_challenge	1	none	acc	↑	0.1664	±	0.0109
		none	acc_norm	↑	0.2159	±	0.0120
arc_easy	1	none	acc	↑	0.3864	±	0.0100
		none	acc_norm	↑	0.3434	±	0.0097
hellaswag	1	none	acc	↑	0.2621	±	0.0044
		none	acc_norm	↑	0.2579	±	0.0044
piqa	1	none	acc	↑	0.5756	±	0.0115
		none	acc_norm	↑	0.5533	±	0.0116

Not the best results, we blame the dataset.

Usage Example

Myosotis-1 is fully compatible with the HuggingFace transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("FWKV/Myosotis-1-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("FWKV/Myosotis-1-base", trust_remote_code=True)

inputs = tok("The quick brown fox", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=150, use_cache=True)
print(tok.decode(out[0]))

Strengths & Limitations

Strengths	Limitations
Blazing Fast CPU Inference: [..] tokens/sec on cheap cloud instances.	Non-Selective Decay: Less mathematically expressive than Mamba or RWKV-v5.
Infinite Context Memory: Clamped decay ensures no hard cutoff.	Factual Hallucination: As a 100M model, fact-retrieval is limited.
No Custom Kernels: Runs anywhere PyTorch runs (AMD, Intel, ARM).	No Token-Shift: Lacks the strong local inductive bias of full RWKV.
Memory Efficient: Constant-size state, scales perfectly with sequence length.	Knowledge Capacity: Smaller than billion-parameter counterparts.
Stable Training: Clamped sigmoid prevents vanishing/exploding gradients.	ROSA Not Included: This is the base recurrent model, separate from the FWKV-ROSA research paper.

Technical References

Inspirations: RWKV (Peng et al.), S4/S5 (Gu et al.), and the Transformer FFN sandwich.

Licensing & Acknowledgements

License: Apache 2.0

This model is dedicated to the open-source community. Built by Daniel B. (FlameF0X) using vast.ai, PyTorch, and the Hugging Face ecosystem.

"Simplicity is all you need."

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