{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--opt-125m","slug":"facebook--opt-125m","name":"opt-125m","type":"model","url":"https://huggingface.co/facebook/opt-125m","page_url":"https://unfragile.ai/facebook--opt-125m","categories":["text-writing"],"tags":["transformers","pytorch","tf","jax","opt","text-generation","en","arxiv:2205.01068","arxiv:2005.14165","license:other","text-generation-inference","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-facebook--opt-125m__cap_0","uri":"capability://text.generation.language.autoregressive.text.generation.with.transformer.decoder.architecture","name":"autoregressive text generation with transformer decoder architecture","description":"Generates text token-by-token using a 12-layer transformer decoder with causal self-attention masking, processing input sequences through learned embeddings and positional encodings to produce contextually coherent continuations. The model uses standard transformer decoding patterns (greedy, beam search, or sampling) implemented via HuggingFace's generation API, supporting batch inference across multiple sequences simultaneously with configurable max_length and temperature parameters.","intents":["Generate natural language text completions from a prompt or context","Build a lightweight language model for resource-constrained environments","Fine-tune a pre-trained decoder for domain-specific text generation tasks","Benchmark transformer performance on consumer hardware"],"best_for":["Developers building chatbots or text completion features on edge devices or low-cost cloud instances","Researchers prototyping language model architectures without massive compute budgets","Teams needing a permissively-licensed open-source baseline for fine-tuning"],"limitations":["125M parameters limits context understanding and reasoning depth compared to larger models (GPT-3 175B, LLaMA-7B); struggles with multi-step reasoning and complex instructions","No instruction-tuning or RLHF alignment — generates raw, unfiltered text without safety guardrails or instruction-following behavior","Single-language (English) training limits multilingual capability; poor performance on non-English prompts","Requires 256MB+ GPU memory or CPU inference is slow (~50-100 tokens/sec on single CPU core); batch inference adds latency overhead"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.6+ or JAX (model supports all three frameworks)","HuggingFace transformers library 4.0+","2GB+ disk space for model weights (fp32) or 500MB (fp16)","4GB+ RAM for inference; 8GB+ recommended for batch processing"],"input_types":["text (raw string prompts)","tokenized input_ids (integer tensor)","attention_mask (optional, for padding handling)"],"output_types":["text (decoded token sequences)","logits (raw model output probabilities per token)","token_ids (integer tensor of generated tokens)"],"categories":["text-generation-language","transformer-decoder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_1","uri":"capability://tool.use.integration.multi.framework.model.serialization.and.inference","name":"multi-framework model serialization and inference","description":"Supports loading and inference across PyTorch, TensorFlow, and JAX frameworks through HuggingFace's unified model hub interface, automatically handling weight conversion and framework-specific optimizations. The model weights are stored in a single canonical format (safetensors or PyTorch pickle) and transparently converted at load time based on the target framework, enabling developers to switch inference backends without retraining or re-downloading weights.","intents":["Deploy the same model across heterogeneous infrastructure (PyTorch on GPU, TensorFlow on TPU, JAX for research)","Integrate OPT into existing ML pipelines regardless of framework choice","Benchmark inference performance across frameworks on identical hardware"],"best_for":["ML teams with mixed-framework codebases (PyTorch research + TensorFlow production)","Organizations evaluating framework-specific optimizations (e.g., TensorFlow Lite for mobile)","Researchers comparing inference efficiency across JAX, PyTorch, and TensorFlow"],"limitations":["Framework conversion adds ~5-10 second load time on first inference; subsequent loads cached locally","TensorFlow and JAX implementations may lag PyTorch in optimization updates; not all generation features available in all frameworks","No automatic quantization or pruning across frameworks — must manually apply framework-specific optimization tools"],"requires":["PyTorch 1.9+ OR TensorFlow 2.6+ OR JAX 0.3.0+ (at least one)","HuggingFace transformers 4.0+","Framework-specific CUDA/cuDNN if using GPU acceleration"],"input_types":["text (framework-agnostic)","pre-tokenized tensors (framework-specific dtype)"],"output_types":["framework-native tensors (torch.Tensor, tf.Tensor, jax.Array)","text (decoded output)"],"categories":["tool-use-integration","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_2","uri":"capability://text.generation.language.prompt.based.few.shot.and.zero.shot.text.generation","name":"prompt-based few-shot and zero-shot text generation","description":"Generates text continuations from arbitrary prompts without task-specific fine-tuning, using in-context learning patterns where the model infers task intent from prompt structure and examples. The model processes the full prompt as context (up to 2048 token limit) and generates tokens autoregressively, allowing developers to specify tasks via natural language instructions or example demonstrations without modifying model weights.","intents":["Perform zero-shot text generation tasks (summarization, translation, Q&A) by crafting effective prompts","Implement few-shot learning by providing 1-5 examples in the prompt","Build task-agnostic text generation pipelines that adapt to new tasks via prompt engineering"],"best_for":["Developers prototyping NLP applications without labeled training data","Teams exploring prompt engineering techniques on a lightweight model","Researchers studying in-context learning behavior in smaller transformer models"],"limitations":["Zero-shot performance is weak compared to instruction-tuned models (Alpaca, GPT-3.5); requires careful prompt engineering to achieve reasonable results","Few-shot learning effectiveness degrades with task complexity; 125M parameters insufficient for reasoning-heavy tasks even with examples","No built-in prompt optimization or automatic few-shot example selection — manual prompt crafting required","Context window limited to 2048 tokens; longer prompts with many examples will be truncated"],"requires":["Python 3.7+","HuggingFace transformers 4.0+","Understanding of prompt engineering best practices"],"input_types":["text (natural language prompts with optional examples)"],"output_types":["text (model-generated continuation)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_3","uri":"capability://code.generation.editing.fine.tuning.and.parameter.efficient.adaptation","name":"fine-tuning and parameter-efficient adaptation","description":"Supports full model fine-tuning and parameter-efficient methods (LoRA, prefix tuning) via HuggingFace Trainer API and PEFT library, enabling developers to adapt the pre-trained model to downstream tasks by updating weights or inserting trainable adapters. The model's 125M parameters make full fine-tuning feasible on consumer GPUs (8GB VRAM), while LoRA reduces trainable parameters to <1M for memory-constrained scenarios.","intents":["Fine-tune OPT on domain-specific text data (e.g., customer support, technical documentation)","Adapt the model to new languages or writing styles with limited labeled data","Create multiple task-specific variants from a single base model using LoRA adapters"],"best_for":["Teams with domain-specific text corpora wanting to customize a lightweight model","Researchers studying fine-tuning efficiency on small models","Developers building multi-tenant systems where per-customer LoRA adapters reduce storage overhead"],"limitations":["Full fine-tuning requires 8GB+ GPU memory; LoRA reduces this to 4GB but adds inference latency (~5-10%)","Fine-tuning on small datasets (<10K examples) risks overfitting; no built-in regularization or early stopping heuristics","Catastrophic forgetting of pre-training knowledge when fine-tuning on narrow domains; requires careful learning rate tuning","No automatic hyperparameter search; developers must manually tune learning rate, batch size, and LoRA rank"],"requires":["Python 3.7+","PyTorch 1.9+ (TensorFlow/JAX fine-tuning less mature)","HuggingFace transformers 4.0+ and PEFT library","GPU with 8GB+ VRAM for full fine-tuning, 4GB+ for LoRA","Labeled training data (minimum 1K examples recommended)"],"input_types":["text (training examples)","structured data (input-output pairs for supervised fine-tuning)"],"output_types":["fine-tuned model weights","LoRA adapter weights (<1MB per adapter)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_4","uri":"capability://text.generation.language.batch.and.streaming.inference.with.configurable.decoding.strategies","name":"batch and streaming inference with configurable decoding strategies","description":"Processes multiple prompts in parallel (batch inference) and supports multiple decoding strategies (greedy, beam search, nucleus sampling, temperature-based sampling) via HuggingFace's generation API. Developers can configure max_length, temperature, top_p, top_k, and repetition_penalty parameters to control output diversity and quality, with streaming support for real-time token-by-token output in web applications.","intents":["Generate multiple text completions in parallel for throughput optimization","Implement diverse beam search to generate multiple candidate outputs for ranking or filtering","Stream generated text to users in real-time (e.g., chatbot UI) without waiting for full completion","Control output randomness and diversity via temperature and sampling parameters"],"best_for":["Backend services processing high-volume text generation requests (batch inference)","Real-time applications requiring streaming output (chatbots, code completion)","Researchers exploring decoding strategy impact on generation quality"],"limitations":["Batch inference throughput limited by GPU memory; batch_size > 32 requires 16GB+ VRAM","Beam search with beam_width > 4 adds 3-5x latency overhead; greedy decoding is fastest but lower quality","Streaming adds ~50-100ms latency per token due to HTTP/WebSocket overhead; not suitable for sub-100ms latency requirements","No built-in batching optimization (dynamic batching, request queuing); requires manual implementation for production use"],"requires":["Python 3.7+","HuggingFace transformers 4.0+","GPU with sufficient VRAM for batch_size (8GB for batch_size=16, 16GB for batch_size=32)","For streaming: web framework (FastAPI, Flask) with WebSocket support"],"input_types":["text (single or batch prompts)","configuration dict (temperature, top_p, max_length, etc.)"],"output_types":["text (generated completions)","token stream (for streaming inference)","logits (raw probabilities per token)"],"categories":["text-generation-language","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_5","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization and model compression for edge deployment","description":"Supports post-training quantization (INT8, INT4) and knowledge distillation via libraries like bitsandbytes and GPTQ, reducing model size from 500MB (fp16) to 100-200MB (INT4) while maintaining inference speed. Quantized models run on CPU or low-end GPUs (2GB VRAM), enabling deployment on edge devices, mobile, and resource-constrained cloud instances without significant quality degradation.","intents":["Deploy OPT on edge devices (Raspberry Pi, mobile phones) with <500MB memory footprint","Reduce inference latency on CPU-only environments by 2-3x via quantization","Lower cloud infrastructure costs by running quantized models on cheaper instance types"],"best_for":["Developers building on-device AI features (mobile apps, IoT devices)","Teams optimizing inference cost on cloud infrastructure","Researchers studying quantization impact on small model performance"],"limitations":["INT4 quantization reduces output quality by 5-15% (measured by perplexity) compared to fp16; noticeable degradation on reasoning tasks","Quantization requires careful calibration on representative data; poor calibration leads to significant quality loss","No automatic quantization — requires manual application of bitsandbytes or GPTQ tools; no built-in quantization in HuggingFace transformers","Quantized models not compatible with fine-tuning; must fine-tune fp16 model then quantize"],"requires":["Python 3.7+","bitsandbytes (for INT8) or GPTQ (for INT4)","HuggingFace transformers 4.0+","For edge deployment: target device SDK (e.g., ONNX Runtime, TensorFlow Lite)"],"input_types":["fp16 or fp32 model weights"],"output_types":["quantized model weights (INT8 or INT4)","quantization config (calibration parameters)"],"categories":["automation-workflow","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_6","uri":"capability://memory.knowledge.embeddings.extraction.for.semantic.search.and.similarity","name":"embeddings extraction for semantic search and similarity","description":"Extracts dense vector representations (embeddings) from intermediate transformer layers via HuggingFace's feature extraction API, enabling semantic similarity search, clustering, and retrieval-augmented generation (RAG) workflows. Developers can extract embeddings from any layer (typically the final hidden state) and use them with vector databases (Pinecone, Weaviate, FAISS) for semantic search without additional embedding models.","intents":["Build semantic search systems over text corpora using OPT embeddings","Cluster similar documents or text snippets for content organization","Implement retrieval-augmented generation (RAG) by retrieving relevant context via embedding similarity"],"best_for":["Teams building semantic search without dedicated embedding models","Researchers studying embedding quality from smaller transformer models","Developers implementing RAG systems with lightweight models"],"limitations":["Embedding quality lower than dedicated embedding models (e.g., all-MiniLM-L6-v2); 125M parameters insufficient for nuanced semantic understanding","Embeddings not fine-tuned for specific domains; generic embeddings may not capture domain-specific similarity","No built-in vector database integration; requires manual integration with FAISS, Pinecone, or Weaviate","Embedding extraction adds ~100-200ms per document; not suitable for real-time embedding generation at scale"],"requires":["Python 3.7+","HuggingFace transformers 4.0+","Vector database (FAISS, Pinecone, Weaviate) for similarity search","GPU optional but recommended for batch embedding extraction"],"input_types":["text (documents or queries)"],"output_types":["dense vectors (768-dimensional embeddings from final hidden state)","similarity scores (cosine similarity between vectors)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--opt-125m__cap_7","uri":"capability://data.processing.analysis.model.evaluation.and.benchmarking.on.standard.nlp.tasks","name":"model evaluation and benchmarking on standard nlp tasks","description":"Provides pre-computed evaluation metrics on standard NLP benchmarks (LAMBADA, HellaSwag, MMLU, WikiText) via HuggingFace Model Card, enabling developers to assess model performance without running expensive evaluations. The model can be evaluated on custom tasks using HuggingFace Evaluate library, supporting metrics like perplexity, BLEU, ROUGE, and task-specific accuracy with minimal code.","intents":["Compare OPT-125M performance against other models on standard benchmarks","Evaluate fine-tuned OPT variants on domain-specific tasks","Measure inference quality degradation from quantization or other optimizations"],"best_for":["Researchers benchmarking model performance across model sizes","Teams validating fine-tuned models before production deployment","Developers assessing quantization impact on downstream task performance"],"limitations":["Pre-computed benchmarks may not reflect performance on custom domains; requires custom evaluation","Standard benchmarks (LAMBADA, HellaSwag) not representative of all use cases; task-specific evaluation needed","Evaluation on large benchmarks (MMLU) requires significant compute; no built-in evaluation caching","No automatic hyperparameter tuning for evaluation; manual configuration of evaluation metrics and thresholds"],"requires":["Python 3.7+","HuggingFace transformers and evaluate libraries","GPU recommended for efficient benchmark evaluation"],"input_types":["benchmark datasets (LAMBADA, HellaSwag, MMLU, WikiText)","custom evaluation datasets"],"output_types":["evaluation metrics (perplexity, accuracy, BLEU, ROUGE scores)","benchmark comparison tables"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.6+ or JAX (model supports all three frameworks)","HuggingFace transformers library 4.0+","2GB+ disk space for model weights (fp32) or 500MB (fp16)","4GB+ RAM for inference; 8GB+ recommended for batch processing","PyTorch 1.9+ OR TensorFlow 2.6+ OR JAX 0.3.0+ (at least one)","HuggingFace transformers 4.0+","Framework-specific CUDA/cuDNN if using GPU acceleration","Understanding of prompt engineering best practices","PyTorch 1.9+ (TensorFlow/JAX fine-tuning less mature)"],"failure_modes":["125M parameters limits context understanding and reasoning depth compared to larger models (GPT-3 175B, LLaMA-7B); struggles with multi-step reasoning and complex instructions","No instruction-tuning or RLHF alignment — generates raw, unfiltered text without safety guardrails or instruction-following behavior","Single-language (English) training limits multilingual capability; poor performance on non-English prompts","Requires 256MB+ GPU memory or CPU inference is slow (~50-100 tokens/sec on single CPU core); batch inference adds latency overhead","Framework conversion adds ~5-10 second load time on first inference; subsequent loads cached locally","TensorFlow and JAX implementations may lag PyTorch in optimization updates; not all generation features available in all frameworks","No automatic quantization or pruning across frameworks — must manually apply framework-specific optimization tools","Zero-shot performance is weak compared to instruction-tuned models (Alpaca, GPT-3.5); requires careful prompt engineering to achieve reasonable results","Few-shot learning effectiveness degrades with task complexity; 125M parameters insufficient for reasoning-heavy tasks even with examples","No built-in prompt optimization or automatic few-shot example selection — manual prompt crafting required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8685875527515847,"quality":0.26,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:48.039Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":7912032,"model_likes":249}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--opt-125m","compare_url":"https://unfragile.ai/compare?artifact=facebook--opt-125m"}},"signature":"Se4zFWLfjsadCIKmkRg3u42x+KVOiW7BfN1+Wa/DW1PlwPPq2CXjTFVix6IjDlQ0kR8DAW9vOfSsAfu8KHLQBQ==","signedAt":"2026-06-20T18:05:02.281Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--opt-125m","artifact":"https://unfragile.ai/facebook--opt-125m","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--opt-125m","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"}}