{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tinyllama","slug":"tinyllama","name":"TinyLlama","type":"model","url":"https://github.com/jzhang38/TinyLlama","page_url":"https://unfragile.ai/tinyllama","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tinyllama__cap_0","uri":"capability://text.generation.language.1.1b.parameter.language.model.inference.with.llama.compatible.architecture","name":"1.1b parameter language model inference with llama-compatible architecture","description":"Executes text generation using a 1.1 billion parameter transformer model with 22 layers, 32 attention heads organized via Grouped Query Attention (4 query groups), 2048 embedding dimension, and 2048 token sequence length. Implements the same tokenizer and architectural patterns as Llama 2, enabling direct compatibility with Llama ecosystem tools while maintaining 10-15x smaller memory footprint than 13B+ models. Supports both base pretrained checkpoints (trained on up to 3 trillion tokens) and supervised fine-tuned chat variants for conversational tasks.","intents":["Deploy a capable language model on edge devices with <4GB memory constraints","Run inference locally without cloud API dependencies or latency overhead","Integrate a Llama-compatible model into existing Llama-based tooling and frameworks","Benchmark language model capabilities on resource-constrained hardware (mobile, embedded systems)"],"best_for":["Edge device developers building on-device AI (mobile, IoT, embedded systems)","Researchers studying model scaling laws and efficiency trade-offs","Teams requiring local inference without cloud dependencies","Developers building privacy-critical applications where data cannot leave device"],"limitations":["Context window limited to 2048 tokens — insufficient for long-document analysis or multi-turn conversations exceeding ~1500 tokens of history","Grouped Query Attention reduces model expressiveness compared to full multi-head attention — measurable performance gap on complex reasoning tasks","Training data cutoff (3 trillion tokens on SlimPajama + Starcoderdata) means knowledge limited to pre-training date; no real-time information","Inference speed on CPU-only systems (e.g., older laptops) drops to ~5-10 tokens/sec, making interactive use impractical without GPU acceleration"],"requires":["Python 3.8+","PyTorch 1.13+ or compatible inference framework (llama.cpp, vLLM, Ollama)","4GB+ RAM for 4-bit quantized inference, 8GB+ for full precision","Optional: GPU with 2GB+ VRAM for acceptable inference speed (A40, RTX 3060, M1/M2 Pro)"],"input_types":["text (prompts, conversation history, system instructions)","structured prompt templates (chat format with system/user/assistant roles)"],"output_types":["text (generated completions, chat responses)","token logits (for advanced sampling strategies)","embeddings (via intermediate layer extraction)"],"categories":["text-generation-language","edge-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_1","uri":"capability://automation.workflow.progressive.checkpoint.based.model.training.with.intermediate.evaluation","name":"progressive checkpoint-based model training with intermediate evaluation","description":"Implements a training pipeline that releases model checkpoints at 7 progressive stages (105B, 503B, 1T, 1.5T, 2T, 2.5T, 3T tokens) with corresponding performance metrics (commonsense reasoning scores tracked via MMLU-style benchmarks). Uses cosine learning rate schedule (4e-4 initial, 2000 warmup steps) with 2M token batch size (2048 sequence length × 1024 batch size) across 16 A100-40G GPUs. Enables researchers to analyze scaling laws and select optimal checkpoint for downstream fine-tuning without retraining from scratch.","intents":["Analyze how model capability scales with training tokens to inform architecture decisions","Select intermediate checkpoint for fine-tuning based on performance-efficiency trade-off","Reproduce training methodology for custom model variants with different architectures","Benchmark training efficiency and identify hardware bottlenecks in large-scale pretraining"],"best_for":["ML researchers studying scaling laws and compute-optimal training","Teams fine-tuning models for domain-specific tasks (medical, legal, code)","Infrastructure engineers optimizing distributed training pipelines","Academic groups with access to multi-GPU clusters (8+ A100s)"],"limitations":["Requires 16 A100-40G GPUs minimum for reproduction — estimated cost $50k-100k in cloud compute for full 3T token training","Training data ratio fixed at 7:3 natural language to code — not customizable without retraining entire pipeline","Checkpoints released at fixed intervals; no ability to extract intermediate models between published steps without custom training infrastructure","Batch size of 2M tokens assumes distributed training setup; single-GPU training requires gradient accumulation reducing effective throughput by 10-100x"],"requires":["PyTorch 1.13+ with distributed training support (torch.distributed)","16x A100-40G GPUs or equivalent (V100s would require 2-3x longer training)","SlimPajama dataset (excluding GitHub) + Starcoderdata (total ~950B tokens, requires ~500GB storage)","CUDA 11.8+ and cuDNN 8.6+","Monitoring infrastructure (Weights & Biases, TensorBoard, or custom logging)"],"input_types":["raw text corpora (SlimPajama, Starcoderdata format)","tokenized datasets (pre-tokenized with Llama 2 tokenizer)","training configuration (YAML/JSON with hyperparameters)"],"output_types":["model checkpoints (PyTorch .pt files, HuggingFace safetensors format)","training logs (loss curves, throughput metrics, validation scores)","evaluation metrics (commonsense reasoning scores, perplexity, downstream task performance)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_10","uri":"capability://automation.workflow.research.grade.model.checkpoints.with.reproducible.training.configuration","name":"research-grade model checkpoints with reproducible training configuration","description":"Releases all 7 base model checkpoints with complete training configuration (hyperparameters, data sources, hardware setup, learning rate schedule) documented in README and EVAL.md, enabling full reproducibility of training process and checkpoint selection. Configuration includes batch size (2M tokens), learning rate (4e-4 with cosine schedule, 2000 warmup steps), hardware (16 A100-40G GPUs), and data composition (7:3 NL:code ratio), allowing researchers to reproduce training or adapt methodology for custom models.","intents":["Reproduce TinyLlama training from scratch for verification or custom variants","Understand training methodology and hyperparameter choices","Adapt training pipeline for different model sizes or data compositions","Publish research using TinyLlama with full methodological transparency"],"best_for":["Academic researchers requiring reproducible training methodology","Teams building custom model variants with documented baselines","Organizations publishing research using TinyLlama","Infrastructure engineers implementing training pipelines"],"limitations":["Reproducibility requires 16 A100-40G GPUs — prohibitive cost ($50k-100k) limits reproduction to well-funded teams","Training takes ~90 days on specified hardware — impractical for rapid iteration or experimentation","Hyperparameters optimized for 16 A100s — may not transfer to different hardware (e.g., H100s, TPUs) without retuning","Documentation in README/EVAL.md may lack implementation details — requires reading source code for full reproducibility"],"requires":["16x A100-40G GPUs (or equivalent compute, e.g., 32x V100s with 2-3x longer training)","PyTorch 1.13+ with distributed training support","SlimPajama + Starcoderdata datasets (~500GB storage)","CUDA 11.8+ and cuDNN 8.6+","~$50k-100k cloud compute budget for full training"],"input_types":["training configuration (hyperparameters, data sources)","raw datasets (SlimPajama, Starcoderdata)","hardware specification (GPU type, count, interconnect)"],"output_types":["model checkpoints (at 7 training stages)","training logs (loss curves, throughput metrics)","evaluation results (performance metrics per checkpoint)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_2","uri":"capability://text.generation.language.supervised.fine.tuning.for.chat.and.instruction.following.with.llama.2.compatibility","name":"supervised fine-tuning for chat and instruction-following with llama 2 compatibility","description":"Applies instruction-tuning and chat fine-tuning to base pretrained checkpoints using supervised learning on curated instruction-response pairs, producing chat-optimized variants (Chat-v0.1, v0.3, v0.4) derived from 503B, 1T, and 1.5T token base models respectively. Maintains Llama 2 chat template format (system/user/assistant role markers) enabling drop-in compatibility with existing chat inference frameworks. Fine-tuned models show measurable improvement in instruction adherence and conversational coherence compared to base models (e.g., Chat-v0.4 achieves 52.30 commonsense score vs 51.28 for base 1.5T model).","intents":["Deploy a chat-optimized model for conversational AI without building custom fine-tuning infrastructure","Fine-tune TinyLlama on proprietary instruction datasets for domain-specific assistants","Benchmark instruction-following capability across different model scales","Integrate chat models into existing Llama 2-compatible chat frameworks (LM Studio, Ollama, vLLM)"],"best_for":["Product teams building chatbot features with local inference requirements","Researchers studying instruction-tuning effectiveness on small models","Teams migrating from larger models (7B+) to edge-deployable alternatives","Developers building domain-specific assistants (customer support, technical help)"],"limitations":["Chat models trained on generic instruction datasets — may not reflect domain-specific terminology or conventions without additional fine-tuning","Performance gap vs 7B+ chat models on complex multi-step reasoning (e.g., math word problems, code generation with multiple dependencies)","No built-in safety fine-tuning (RLHF/DPO) — model may generate harmful content without additional safety layers","Chat template locked to Llama 2 format — incompatible with other chat formats (ChatML, Alpaca) without post-processing"],"requires":["Base TinyLlama checkpoint (503B, 1T, or 1.5T tokens)","Instruction dataset (10k-100k examples) in Llama 2 chat format","PyTorch 1.13+ with LoRA/QLoRA support (peft library) for efficient fine-tuning","4GB+ GPU VRAM for LoRA fine-tuning, 8GB+ for full fine-tuning","Optional: Weights & Biases or similar for tracking fine-tuning runs"],"input_types":["instruction-response pairs (JSON/JSONL format with system/user/assistant roles)","base model checkpoint (PyTorch or HuggingFace format)","fine-tuning hyperparameters (learning rate, epochs, LoRA rank)"],"output_types":["fine-tuned model checkpoint (compatible with base model inference)","training metrics (loss curves, validation perplexity)","evaluation results (BLEU, ROUGE, or custom instruction-following metrics)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_3","uri":"capability://text.generation.language.quantized.inference.optimization.for.consumer.hardware.4.bit.8.bit","name":"quantized inference optimization for consumer hardware (4-bit, 8-bit)","description":"Supports multiple quantization backends (llama.cpp with GGUF format, vLLM with AWQ/GPTQ, bitsandbytes 4-bit/8-bit) enabling inference on consumer GPUs and CPUs with 4-8x memory reduction. Achieves 71.8 tokens/sec on Mac M2 with 4-bit quantization (batch size 1) and 7,094.5 tokens/sec on A40 GPU with batch size 100 in vLLM, demonstrating practical inference speeds across hardware tiers. Quantization applied post-training without retraining, enabling rapid deployment across diverse hardware without custom optimization per device.","intents":["Run TinyLlama inference on laptop/mobile without GPU (using llama.cpp CPU backend)","Maximize throughput on constrained GPU memory (RTX 3060 12GB, M1/M2 Pro/Max)","Batch inference for production serving with predictable latency","Compare inference performance across quantization strategies (4-bit vs 8-bit vs FP16)"],"best_for":["Individual developers prototyping on consumer hardware (MacBook, gaming laptops)","Startups deploying inference at scale with cost constraints","Teams building offline-first applications (no cloud inference)","Researchers benchmarking quantization impact on model quality"],"limitations":["4-bit quantization introduces ~2-5% accuracy loss on reasoning tasks (measurable on MMLU-style benchmarks) — acceptable for chat but problematic for code generation","Batch inference (vLLM) requires GPU; CPU inference (llama.cpp) limited to ~1-2 tokens/sec on modern CPUs, making real-time chat impractical","Quantization formats not interchangeable — GGUF (llama.cpp) incompatible with GPTQ (vLLM) without conversion, adding deployment complexity","Memory savings don't scale linearly — 4-bit model still requires ~2GB for KV cache in batch inference, limiting concurrent requests"],"requires":["llama.cpp (for CPU/Mac inference) or vLLM (for GPU batch inference)","GGUF quantized model (for llama.cpp) or GPTQ/AWQ quantized model (for vLLM)","2GB+ RAM (4-bit quantized model) or 4GB+ (8-bit quantized model)","Optional: GPU with 2GB+ VRAM for acceptable throughput (RTX 3060, A40, M1 Pro)"],"input_types":["text prompts (plain text or chat format)","quantized model weights (GGUF, GPTQ, or AWQ format)"],"output_types":["text completions (streaming or batch)","token-level probabilities (for sampling strategies)","performance metrics (tokens/sec, latency percentiles)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_4","uri":"capability://automation.workflow.speculative.decoding.for.latency.reduction.in.batch.inference","name":"speculative decoding for latency reduction in batch inference","description":"Implements speculative decoding (draft model + verification) where TinyLlama acts as a fast draft model to generate candidate tokens, verified against a larger model (e.g., Llama 2 7B) to maintain output quality while reducing wall-clock latency. Leverages TinyLlama's fast inference speed (7k+ tokens/sec on A40) to generate multiple candidate tokens per step, with verification rejecting invalid candidates and accepting valid ones, reducing effective latency by 30-50% for batch inference workloads compared to direct large model inference.","intents":["Reduce latency for batch inference serving (e.g., API endpoints handling 100+ concurrent requests)","Maintain output quality of larger models while achieving TinyLlama inference speed","Optimize inference cost by reducing large model inference time","Benchmark speculative decoding effectiveness on different model pairs"],"best_for":["Production inference services requiring <500ms latency for batch requests","Teams with budget constraints wanting to serve large models efficiently","Researchers studying speculative decoding on small-to-large model pairs","Applications where output quality cannot be compromised (customer-facing chat)"],"limitations":["Requires two models in memory simultaneously — total memory footprint ~6-8GB (TinyLlama 4-bit + Llama 2 7B 8-bit), limiting deployment to GPUs with 12GB+ VRAM","Latency reduction depends on draft model quality — if TinyLlama generates poor candidates, verification rejects most, negating speedup (worst case: slower than direct inference)","Speculative decoding incompatible with constrained decoding (e.g., JSON schema enforcement) — requires custom verification logic","Throughput gains diminish with very large batch sizes (>256) where verification becomes bottleneck"],"requires":["TinyLlama model (base or chat variant) quantized to 4-bit","Larger reference model (Llama 2 7B or equivalent) quantized to 8-bit","vLLM or similar framework with speculative decoding support","GPU with 12GB+ VRAM (A40, RTX 4080, H100)","Batch size ≥32 for meaningful latency reduction"],"input_types":["text prompts (batch of 32-256 requests)","inference parameters (temperature, top-p, max tokens)"],"output_types":["text completions (with latency metrics)","acceptance rate metrics (% of draft tokens accepted by verifier)","performance comparison (latency vs direct large model inference)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_5","uri":"capability://text.generation.language.grouped.query.attention.gqa.for.memory.efficient.multi.head.attention","name":"grouped query attention (gqa) for memory-efficient multi-head attention","description":"Implements Grouped Query Attention with 32 attention heads organized into 4 query groups (8 heads per group), reducing KV cache memory from O(batch_size × seq_len × num_heads × head_dim) to O(batch_size × seq_len × num_groups × head_dim). This architectural choice reduces KV cache size by 8x compared to full multi-head attention while maintaining comparable model quality, enabling larger batch sizes and longer sequences on memory-constrained hardware. GQA is applied uniformly across all 22 transformer layers, making it integral to TinyLlama's efficiency profile.","intents":["Maximize batch size on fixed GPU memory (e.g., RTX 3060 12GB)","Enable longer context windows without proportional memory increase","Understand trade-offs between attention mechanism efficiency and model expressiveness","Benchmark GQA impact on inference latency and quality vs full multi-head attention"],"best_for":["Inference engineers optimizing batch size for production serving","Researchers studying attention mechanism trade-offs in small models","Teams deploying on memory-constrained GPUs (mobile, edge devices)","Model architects designing efficient transformer variants"],"limitations":["GQA reduces model expressiveness compared to full multi-head attention — measurable quality gap on complex reasoning tasks (estimated 2-5% accuracy loss on MMLU)","KV cache memory savings only realized during inference; training memory footprint similar to full attention due to gradient computation","Batch size scaling benefits plateau at ~256 batch size (other bottlenecks dominate); diminishing returns beyond this point","GQA incompatible with some attention variants (e.g., sliding window attention, sparse attention patterns) — limits architectural flexibility"],"requires":["PyTorch 1.13+ with custom CUDA kernels for efficient GQA (or use vLLM/TensorRT for optimized inference)","Understanding of transformer attention mechanics to interpret performance trade-offs","GPU with sufficient memory for batch inference (2GB+ for batch size 32, 4GB+ for batch size 128)"],"input_types":["query, key, value tensors (from transformer layer)","attention mask (causal or custom)"],"output_types":["attention output (same shape as input query)","attention weights (optional, for visualization)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_6","uri":"capability://data.processing.analysis.llama.2.tokenizer.compatibility.and.vocabulary.alignment","name":"llama 2 tokenizer compatibility and vocabulary alignment","description":"Uses identical tokenizer to Llama 2 (32k token vocabulary, BPE-based) enabling seamless token-level compatibility with existing Llama ecosystem tools, datasets, and inference frameworks. Tokenizer applied consistently across all training stages (pretraining, fine-tuning, inference) and across all checkpoint variants, ensuring reproducible token sequences and enabling direct comparison with Llama 2 benchmarks. Vocabulary alignment means TinyLlama can process Llama 2 datasets without re-tokenization and vice versa, reducing integration friction.","intents":["Use existing Llama 2 datasets and benchmarks without re-tokenization","Integrate TinyLlama into Llama 2-based inference frameworks without custom tokenizer","Compare model performance fairly with Llama 2 on identical token sequences","Migrate from Llama 2 to TinyLlama with minimal code changes"],"best_for":["Teams already invested in Llama 2 ecosystem (frameworks, datasets, benchmarks)","Researchers comparing model variants with controlled tokenization","Developers building language model applications requiring Llama compatibility","Organizations migrating from Llama 2 7B to TinyLlama for cost/efficiency"],"limitations":["32k vocabulary may be suboptimal for non-English languages (e.g., Chinese, Arabic) — token efficiency lower than language-specific tokenizers","Tokenizer fixed at training time — cannot adapt to domain-specific vocabulary (e.g., medical terminology) without retraining","BPE tokenization produces variable-length token sequences for identical text across different contexts — affects reproducibility in some edge cases","No built-in support for special tokens beyond Llama 2 standard set — custom tokens require manual vocabulary extension"],"requires":["transformers library (HuggingFace) with Llama tokenizer support","Llama 2 tokenizer model file (tokenizer.model, publicly available)","Python 3.8+"],"input_types":["raw text (any language, though optimized for English)","structured text (code, JSON, markdown)"],"output_types":["token IDs (list of integers, 0-32000)","token strings (for debugging/visualization)","token statistics (vocabulary coverage, compression ratio)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_7","uri":"capability://data.processing.analysis.multi.checkpoint.evaluation.and.performance.tracking.across.training.stages","name":"multi-checkpoint evaluation and performance tracking across training stages","description":"Provides published performance metrics (commonsense reasoning scores via MMLU-style benchmarks) for all 7 base model checkpoints (105B, 503B, 1T, 1.5T, 2T, 2.5T, 3T tokens) and 3 chat variants, enabling empirical analysis of scaling laws and checkpoint selection without manual evaluation. Metrics tracked consistently across checkpoints using identical evaluation methodology, allowing direct comparison of model capability progression. Evaluation infrastructure (EVAL.md documentation) enables users to reproduce benchmarks on custom datasets or evaluate fine-tuned variants using same methodology.","intents":["Select optimal checkpoint for fine-tuning based on performance-efficiency trade-off","Analyze scaling laws empirically (how capability scales with training tokens)","Reproduce evaluation methodology for custom models or datasets","Compare TinyLlama performance against other 1B-scale models on standardized benchmarks"],"best_for":["ML researchers studying scaling laws and model efficiency","Teams selecting checkpoint for domain-specific fine-tuning","Practitioners benchmarking TinyLlama against alternatives","Infrastructure teams validating model quality before production deployment"],"limitations":["Evaluation limited to commonsense reasoning (MMLU-style) — doesn't cover code generation, math reasoning, or other specialized tasks","Benchmark scores may not correlate with downstream task performance — high MMLU score doesn't guarantee good chat quality","Evaluation methodology not published in detail — difficult to reproduce exact scores or extend to custom benchmarks","No human evaluation or preference data — automated metrics may not reflect user satisfaction"],"requires":["EVAL.md documentation (published in repository)","Benchmark datasets (MMLU or equivalent, publicly available)","Python 3.8+ with evaluation harness (custom or standard benchmarking tools)","Compute for running inference on all checkpoints (~1-2 hours per checkpoint on A40)"],"input_types":["model checkpoints (base or chat variants)","evaluation datasets (MMLU-style multiple choice questions)","evaluation configuration (batch size, sampling parameters)"],"output_types":["performance metrics (accuracy scores, F1, BLEU, ROUGE)","scaling curves (capability vs training tokens)","checkpoint comparison tables (for decision-making)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_8","uri":"capability://data.processing.analysis.data.preparation.pipeline.with.slimpajama.and.starcoderdata.integration","name":"data preparation pipeline with slimpajama and starcoderdata integration","description":"Implements data preparation workflow combining SlimPajama (natural language, excluding GitHub) and Starcoderdata (code) in 7:3 ratio, with tokenization using Llama 2 tokenizer and batching into 2M token sequences (2048 length × 1024 batch size). Pipeline handles data deduplication, filtering, and shuffling to ensure training stability across 3 trillion tokens. Documented in training configuration enabling users to prepare custom datasets following same methodology for domain-specific pretraining or continued training on custom data.","intents":["Understand data composition and quality decisions behind TinyLlama training","Prepare custom datasets for continued training or domain-specific pretraining","Reproduce training data pipeline for research or model variants","Analyze impact of data ratio (7:3 NL:code) on model capability"],"best_for":["Researchers studying data composition impact on model quality","Teams doing continued training on proprietary datasets","Organizations building domain-specific models (medical, legal, code)","Infrastructure engineers setting up data pipelines for large-scale training"],"limitations":["Data ratio (7:3 NL:code) fixed — cannot easily adjust for different domains without retraining","SlimPajama + Starcoderdata combination optimized for general-purpose models — may be suboptimal for specialized domains","Data preparation pipeline requires significant storage (~500GB for raw data) and compute (tokenization takes hours on multi-core CPU)","No built-in data quality filtering beyond deduplication — may include low-quality or biased content from source datasets"],"requires":["SlimPajama dataset (publicly available, ~627B tokens)","Starcoderdata dataset (publicly available, ~250B tokens)","Python 3.8+ with data processing libraries (pandas, datasets, tokenizers)","500GB+ storage for raw data, 200GB+ for tokenized data","Multi-core CPU or GPU for tokenization (8+ cores recommended)"],"input_types":["raw text files (SlimPajama, Starcoderdata format)","data configuration (ratio, filtering rules, batch size)"],"output_types":["tokenized datasets (PyTorch DataLoader format)","data statistics (token count, vocabulary coverage, deduplication metrics)","batched training data (2M token sequences)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__cap_9","uri":"capability://text.generation.language.hardware.agnostic.model.architecture.enabling.deployment.across.compute.tiers","name":"hardware-agnostic model architecture enabling deployment across compute tiers","description":"Designs 1.1B parameter model with 2048 embedding dimension and 22 transformer layers to fit within memory constraints of diverse hardware (2GB for 4-bit inference on edge, 4GB for 8-bit, 8GB+ for full precision), while maintaining architectural compatibility with Llama 2 (same tokenizer, attention patterns, layer structure). Architecture scales inference throughput from 71.8 tokens/sec on Mac M2 CPU to 7,094.5 tokens/sec on A40 GPU, enabling deployment decisions based on latency/cost trade-offs rather than model retraining.","intents":["Deploy same model across heterogeneous hardware (CPU, mobile GPU, data center GPU) without retraining","Make deployment decisions based on latency/cost/privacy requirements","Benchmark inference performance across hardware tiers to inform infrastructure choices","Build applications with graceful degradation (fallback to CPU if GPU unavailable)"],"best_for":["Teams building cross-platform applications (mobile + cloud)","Organizations with heterogeneous hardware infrastructure","Developers optimizing for cost (CPU inference cheaper than GPU)","Privacy-focused applications requiring on-device inference"],"limitations":["Inference speed varies 100x across hardware tiers (71.8 tok/sec M2 vs 7,094.5 tok/sec A40) — requires different latency SLAs per deployment","Memory footprint scales with quantization level — 4-bit model still requires 2GB, limiting deployment to devices with ≥2GB RAM","Batch inference optimization (vLLM) requires GPU — CPU batch inference impractical (throughput drops to <10 tok/sec)","Architecture fixed at training time — cannot adapt to hardware constraints post-hoc (e.g., reduce layers for mobile)"],"requires":["Model weights (4-bit, 8-bit, or FP16 quantization)","Inference framework compatible with target hardware (llama.cpp for CPU/Mac, vLLM for GPU, Ollama for cross-platform)","2GB+ RAM (minimum for 4-bit inference)","Optional: GPU with 2GB+ VRAM for acceptable throughput"],"input_types":["text prompts","inference parameters (temperature, top-p, max tokens)"],"output_types":["text completions","performance metrics (latency, throughput, memory usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tinyllama__headline","uri":"capability://model.training.compact.language.model.for.edge.deployment","name":"compact language model for edge deployment","description":"TinyLlama is a 1.1 billion parameter language model designed for edge deployment, achieving impressive capabilities while maintaining a compact size, making it ideal for research and practical applications.","intents":["best compact language model","language model for edge deployment","1.1B parameter model for research","efficient language model for consumer hardware","language model trained on 3 trillion tokens"],"best_for":["edge deployment","research purposes"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ or compatible inference framework (llama.cpp, vLLM, Ollama)","4GB+ RAM for 4-bit quantized inference, 8GB+ for full precision","Optional: GPU with 2GB+ VRAM for acceptable inference speed (A40, RTX 3060, M1/M2 Pro)","PyTorch 1.13+ with distributed training support (torch.distributed)","16x A100-40G GPUs or equivalent (V100s would require 2-3x longer training)","SlimPajama dataset (excluding GitHub) + Starcoderdata (total ~950B tokens, requires ~500GB storage)","CUDA 11.8+ and cuDNN 8.6+","Monitoring infrastructure (Weights & Biases, TensorBoard, or custom logging)","16x A100-40G GPUs (or equivalent compute, e.g., 32x V100s with 2-3x longer training)"],"failure_modes":["Context window limited to 2048 tokens — insufficient for long-document analysis or multi-turn conversations exceeding ~1500 tokens of history","Grouped Query Attention reduces model expressiveness compared to full multi-head attention — measurable performance gap on complex reasoning tasks","Training data cutoff (3 trillion tokens on SlimPajama + Starcoderdata) means knowledge limited to pre-training date; no real-time information","Inference speed on CPU-only systems (e.g., older laptops) drops to ~5-10 tokens/sec, making interactive use impractical without GPU acceleration","Requires 16 A100-40G GPUs minimum for reproduction — estimated cost $50k-100k in cloud compute for full 3T token training","Training data ratio fixed at 7:3 natural language to code — not customizable without retraining entire pipeline","Checkpoints released at fixed intervals; no ability to extract intermediate models between published steps without custom training infrastructure","Batch size of 2M tokens assumes distributed training setup; single-GPU training requires gradient accumulation reducing effective throughput by 10-100x","Reproducibility requires 16 A100-40G GPUs — prohibitive cost ($50k-100k) limits reproduction to well-funded teams","Training takes ~90 days on specified hardware — impractical for rapid iteration or experimentation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tinyllama","compare_url":"https://unfragile.ai/compare?artifact=tinyllama"}},"signature":"YrMpE+pBvVUWizwhCZRcIbpGg5GFJzutcoTwmozknf021s+ppqI1VqYBSXrGMiajQnOYNBP4pJIPZKudswyxDQ==","signedAt":"2026-06-22T01:01:35.987Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tinyllama","artifact":"https://unfragile.ai/tinyllama","verify":"https://unfragile.ai/api/v1/verify?slug=tinyllama","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}