SynLlama: Dual Domain Llama Applications

• SynLlama is a dual application of minimally adapted Llama models, addressing both synthesizable molecule generation in computational chemistry and low-latency dialogue in spoken AI.
• It uses focused data curation and fine-tuning on domain-specific corpora, achieving robust retrosynthetic pathway reconstruction and coherent, real-time conversational output.
• The framework demonstrates actionable impact through validated hit expansion in molecule design and measurable performance in human-like interactive dialogue.

SynLlama is the designation for two distinct, high-impact applications of LLMs that leverage Meta’s Llama architectures: (1) in computational chemistry, for synthesizable molecule and analog generation (Sun et al., 16 Mar 2025); and (2) in spoken AI, as a full-duplex, low-latency dialogue agent (Veluri et al., 2024). Both lines share the core approach of minimally adapting a pre-trained Llama model—without fundamental architectural changes—to domain-specific tasks via focused data curation and training regimens. The following analysis covers each system in technical depth and discusses their shared principles and divergent methodologies.

1. SynLlama for Synthesizable Molecule and Analog Generation

SynLlama, in the molecular design context, refers to a fine-tuned Llama 3.2-1B transformer model engineered to generate complete, actionable synthetic routes for small molecules using robust reaction templates and purchasable building blocks. The model ingests prompts containing a target molecule (SMILES format) and outputs a stepwise retrosynthetic JSON, including reaction SMARTS and all required starting materials, facilitating both bottom-up synthesis planning and de novo analog exploration (Sun et al., 16 Mar 2025).

Model Architecture

• Base Model: Meta's Llama 3.2-1B (also evaluated with Llama 3.1-8B). No modifications to transformer layers or attention mechanisms.
• Input/Output Formatting: Prompts include instructions and target SMILES; outputs are JSON objects detailing retrosynthesis steps and leaf-node building blocks.
• Tokenization: Byte-Pair Encoding (BPE), augmented with additional tokens for chemical syntax (e.g., “[C@H]”, “=O”) and reaction demarcators (“<RXN#>”).
• Attention: Default sparse fixed-pattern attention, supporting multi-step syntheses within a single context window without chemistry-specific adaptations.

2. Training Corpus Development and Fine-Tuning Methodology

• Building Blocks: 229,579 Enamine reagents, partitioned via K-means into 128 clusters; one cluster held out to test extrapolation.
• Reaction Templates: 91 curated SMARTS from domain literature (e.g., amide coupling, Suzuki reaction).
• Data Generation: Forward-synthesis enumeration up to depth five, combining compatible templates and building blocks, yielding a combinatorial space of ~10³⁰ routes; up to 2 million examples generated.
• Loss Objective: Cross-entropy (teacher forcing) across tokenized JSON sequences:

$$\mathcal{L}(\theta) = -\sum_{k=1}^{N_{\rm examples}} \sum_{t=1}^{T_k} \log p_{\theta}(x_t^{(k)} | x_{<t}^{(k)})$$

• Hyperparameters: Adam optimizer (β₁=0.9, β₂=0.999), lr=1e-5, batch size 32, 1000-step warmup, ≈240 GPU-hours per full run.

3. Synthesis Planning, Analog Generation, and Reconstruction Pipeline

• Retrosynthetic Planning: Standard prompt elicits JSON-encoded pathways with reaction templates and intermediates.
• Building Block Mapping: Outputs cross-referenced to Enamine. For OOD molecules, KNN search in Morgan fingerprint space within each reaction template context retrieves best purchasable analogs.
• Forward Synthesis: Templates applied sequentially from building blocks, with product selection based on minimum SMILES Levenshtein distance to model prediction.
• Analog Library Generation: Enumerates top-K BB analogs for each step, filters for molecular similarity or docking/SA scores.
• Case Studies: Demonstrated capacity for hit expansion against SARS-CoV-2 Mpro, and analog optimization for iMiner binders, with experimental docking and FEP validation.

4. Benchmarking, Generalization, and Limitations

Performance Metrics

Metric/Set	Training	Test (OOD)	ChEMBL (dist. shift)
Valid JSON	>95%	>95%	92–96%
Good BB Selection	>99%	>99%	—
Reconstructions (Test)	616	>600	160
Analog Similarity	~0.94	~0.94	—
Good Product Recovery	~98%	70–85%	~87%

• Generalization: Robust to unseen BBs; OOD generalization enables use of entirely new, purchasable reagents identified by Molport.
• Limitations: Restriction to 91 templates, no explicit modeling of yields, cost, or selectivity. No reaction condition or fully multi-modal (e.g., 2D/3D structural) input support at present.

5. SynLlama for Full-Duplex Synchronous Dialogue

A second, independent SynLlama instantiation appears in spoken AI, as described in the synchronous LLM (“SyncLLM”) framework for full-duplex interaction (Veluri et al., 2024). Here, SynLlama denotes the deployment of a Llama3-8b adaptation capable of running continuously, chunk-synchronously with real-world clock ticks, supporting rapid, low-latency turn-taking and speech overlap.

Key Technical Details

• Model Base: Llama3-8b (sequence 8192, hidden size 4096).
• Time Sync Mechanism:
  • Periodic speaker sync tokens [S0]/[S1] inserted at chunk boundaries (Δ = 160, 200, or 240 ms) mark wall-clock intervals for each participant.
  • Downstream self-attention and positional encoding unmodified.
• Streaming Scheduler: “Anticipation” mechanism: hallucinate user’s next chunk, predict agent’s next, then replace with real user input at the next interval.
• Training Procedure: Three-stage regimen spanning 193k hours synthetic text dialogue (with Bark-TTS audio), 20k hours long-turn text, and 1.9k hours Fisher dual-channel speech.
• Loss: Next-token cross-entropy over deduplicated, interleaved HuBERT and sync tokens.

6. Evaluation and Results Across Domains

Molecular SynLlama (Sun et al., 16 Mar 2025)

• Reconstruction: Outperforms prior art (SynNet, ChemProjector, Synformer) in reconstructing targets using only commercially available building blocks.
• Analog Similarity: Achieves Morgan/Gobbi/Murcko scaffold similarity ~0.94 on Testing.
• Pharmaceutical Utility: Demonstrated effective hit expansion and local SAR evolution, with docking RMSE on par with experimental uncertainty and strong free-energy correlation.

Synchronous Dialogue SynLlama (Veluri et al., 2024)

• Semantic Coherence: Median perplexity for full-duplex SyncLLM (45–50) approaches ground-truth (30), significantly surpassing dGSLM (100).
• Turn-taking Fidelity: Pearson r for IPU, Pause, FTO (0.4–0.6), outperforming half-duplex baselines.
• Human Ratings: Meaningfulness MOS up to 3.74 (SyncLLM-F), naturalness MOS up to 3.96; two-model interactive setups maintain high dialogue naturalness and meaning.

7. Extensions, Limitations, and Prospects

Both SynLlama instantiations reflect the potential of Llama-based LLMs—minimally adapted with domain-appropriate prompts, tokens, and data—for rapid prototyping of specialized high-performance tools.

• Data/Training Efficiency: In molecule synthesis, strong generalization is achieved with ~2M examples and a 1B-parameter model, compared to 40–100× larger requirements of alternatives.
• System Limitations:
  • Chemical: Limited reaction template diversity; absence of detailed synthetic details.
  • Dialogue: No continuous time embeddings; scheduler corrections for clock drift unimplemented; synthetic TTS/simple silence for pretraining data.
• Future Directions:
  • Chemistry: Expand template sets, incorporate richer reaction annotation (conditions, yields, cost), and investigate property-conditioned training.
  • Dialogue: Integrate real-valued time embeddings, feedback-driven scheduling, and distributional latency modeling.

These lines of research demonstrate the adaptability of foundation models via the SynLlama recipe—lightweight prompt and token modifications powered by substantial, domain-curated data streams—to yield models that bridge generative and operational domains in both computational science and conversational AI (Sun et al., 16 Mar 2025, Veluri et al., 2024).