{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-qwen--qwen3-4b-instruct-2507","slug":"qwen--qwen3-4b-instruct-2507","name":"Qwen3-4B-Instruct-2507","type":"model","url":"https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507","page_url":"https://unfragile.ai/qwen--qwen3-4b-instruct-2507","categories":["chatbots-assistants"],"tags":["transformers","safetensors","qwen3","text-generation","conversational","arxiv:2505.09388","license:apache-2.0","eval-results","text-generation-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.multi.turn.conversation.support","name":"instruction-following text generation with multi-turn conversation support","description":"Generates contextually relevant text responses to user instructions using a transformer-based architecture optimized for instruction-following tasks. The model processes input tokens through 32 transformer layers with attention mechanisms, maintaining conversation history across multiple turns to generate coherent, instruction-aligned outputs. Supports both single-turn and multi-turn dialogue patterns with automatic context windowing.","intents":["Build a conversational chatbot that understands and follows user instructions across multiple turns","Generate contextually appropriate responses to open-ended prompts without external retrieval","Create an AI assistant that maintains conversation state and adapts responses based on dialogue history","Deploy a lightweight instruction-following model for edge devices or cost-constrained environments"],"best_for":["Developers building lightweight chatbot applications with <4B parameter budgets","Teams deploying conversational AI on resource-constrained devices (mobile, edge servers)","Open-source projects requiring permissive Apache 2.0 licensing","Researchers benchmarking instruction-following performance on smaller model scales"],"limitations":["4B parameter scale limits reasoning depth compared to 7B+ models — struggles with multi-step logical problems","Context window size not explicitly documented — likely 4K-8K tokens, limiting long document processing","No built-in retrieval augmentation — cannot access external knowledge bases or real-time information","Training data cutoff (likely 2024 or earlier) means no knowledge of recent events","Single-GPU inference recommended; multi-GPU scaling not optimized for models this size"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Transformers library 4.40+","Minimum 8GB RAM for inference (16GB recommended for batch processing)","CUDA 11.8+ for GPU acceleration (optional but strongly recommended)"],"input_types":["text (natural language instructions)","text (multi-turn conversation history in standard chat format)","text (system prompts for behavior customization)"],"output_types":["text (generated response)","text (streaming tokens for real-time output)","structured data (logits/probabilities for token selection)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_1","uri":"capability://text.generation.language.streaming.token.generation.with.configurable.sampling.strategies","name":"streaming token generation with configurable sampling strategies","description":"Generates text tokens sequentially with support for multiple decoding strategies (greedy, top-k, top-p, temperature scaling) to control output diversity and coherence. The model uses a token-by-token generation loop where each new token is sampled from the probability distribution over the vocabulary, with sampling parameters allowing fine-grained control over creativity vs determinism. Streaming output enables real-time token delivery without waiting for full sequence completion.","intents":["Stream generated text to users in real-time for responsive chatbot experiences","Control output randomness and diversity through temperature and sampling parameters","Generate multiple candidate responses by adjusting top-k or top-p thresholds","Implement deterministic outputs for reproducible testing or production logging"],"best_for":["Web/mobile applications requiring real-time text streaming to users","Interactive applications where response latency is critical","Systems needing deterministic outputs for testing or compliance logging","Applications experimenting with different creativity levels (e.g., creative writing vs factual Q&A)"],"limitations":["Streaming adds ~50-100ms latency per token on CPU; GPU reduces to 10-30ms but requires CUDA setup","Temperature scaling is applied at sampling time — cannot retroactively adjust creativity of already-generated tokens","Top-k and top-p filtering reduce vocabulary diversity but may truncate valid low-probability tokens","No built-in beam search or diverse beam search — single-path generation only","Sampling strategies are stateless — no memory of previous samples for avoiding repetition"],"requires":["Python 3.8+","Transformers library 4.40+ with streaming support","PyTorch 2.0+","Optional: CUDA 11.8+ for GPU acceleration"],"input_types":["text (prompt)","numeric parameters (temperature: 0.0-2.0, top_k: 1-50, top_p: 0.0-1.0)"],"output_types":["text stream (tokens delivered incrementally)","numeric (logits/probabilities for each token)"],"categories":["text-generation-language","streaming-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_10","uri":"capability://text.generation.language.fine.tuning.and.parameter.efficient.adaptation.through.lora.and.qlora","name":"fine-tuning and parameter-efficient adaptation through lora and qlora","description":"Enables efficient fine-tuning on custom datasets using Low-Rank Adaptation (LoRA) or Quantized LoRA (QLoRA), which adds small trainable matrices to frozen model weights rather than updating all parameters. LoRA reduces trainable parameters from 4B to ~1-10M (0.025-0.25% of original), enabling fine-tuning on consumer GPUs. QLoRA further reduces memory by quantizing the base model to INT4 while keeping LoRA weights in higher precision.","intents":["Fine-tune the model on domain-specific data without full retraining","Adapt the model to specific tasks or writing styles with limited data","Create multiple specialized versions of the model for different use cases","Fine-tune on consumer GPUs (8GB-16GB VRAM) without enterprise hardware"],"best_for":["Teams with domain-specific data wanting to customize the model","Researchers experimenting with fine-tuning on limited budgets","Applications requiring multiple specialized model variants","Scenarios where full model retraining is infeasible"],"limitations":["LoRA quality depends on rank hyperparameter — too low rank loses expressiveness, too high rank approaches full fine-tuning cost","Fine-tuning requires careful hyperparameter tuning (learning rate, rank, alpha) — poor tuning can degrade performance","LoRA adapters are model-specific — cannot transfer adapters between different base models","Inference with LoRA requires merging adapters into base model or loading both during inference, adding complexity","Fine-tuning data quality is critical — noisy or biased data can significantly degrade performance"],"requires":["Python 3.8+","PyTorch 2.0+","peft library (Parameter-Efficient Fine-Tuning) for LoRA support","Transformers library 4.40+","GPU with 8GB+ VRAM for QLoRA, 16GB+ for standard LoRA","Fine-tuning dataset (typically 100+ examples for meaningful adaptation)"],"input_types":["text (training examples)","text (labels or target outputs)"],"output_types":["LoRA adapter weights (typically 1-50MB)","fine-tuned model (merged weights)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_11","uri":"capability://text.generation.language.multi.modal.prompt.understanding.through.text.only.processing.with.vision.descriptions","name":"multi-modal prompt understanding through text-only processing with vision descriptions","description":"While Qwen3-4B-Instruct is text-only, it can process descriptions or captions of images provided as text input, enabling indirect multi-modal understanding. The model processes text descriptions of visual content (e.g., 'Image shows a cat sitting on a chair') and generates responses based on the description. This is not true multi-modal processing but rather text-based reasoning about visual content.","intents":["Answer questions about images when image descriptions are provided as text","Process visual content from systems that generate image captions or OCR output","Build applications that combine image understanding from external vision models with Qwen3's language capabilities","Reason about visual scenarios described in natural language"],"best_for":["Applications combining external vision models with language understanding","Systems processing image captions or OCR output","Scenarios where image descriptions are available but not raw images","Research on vision-language understanding through text descriptions"],"limitations":["Not true multi-modal — requires external image processing (vision model, OCR, or manual description)","Quality depends entirely on quality of image descriptions — poor descriptions lead to poor understanding","Cannot process raw images directly — requires separate vision model or manual annotation","No visual grounding — cannot point to specific regions in images or understand spatial relationships directly","Latency is higher than true multi-modal models due to separate vision processing step"],"requires":["Python 3.8+","Transformers library 4.40+","External vision model for image processing (e.g., CLIP, LLaVA, or manual image descriptions)","Image processing pipeline to convert images to text descriptions"],"input_types":["text (image description or caption)"],"output_types":["text (response based on image description)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_2","uri":"capability://text.generation.language.batch.inference.with.dynamic.batching.and.padding.optimization","name":"batch inference with dynamic batching and padding optimization","description":"Processes multiple input sequences simultaneously through the transformer, automatically padding variable-length inputs to the same length and using attention masks to ignore padding tokens. The model leverages PyTorch's batching and CUDA's parallel processing to compute embeddings and logits for multiple sequences in a single forward pass, with dynamic batching allowing flexible batch sizes without recompilation. Padding is optimized to minimize wasted computation on padding tokens.","intents":["Process multiple user queries or documents in parallel for throughput optimization","Evaluate model performance on benchmark datasets with variable-length inputs","Build production inference pipelines that maximize GPU utilization across requests","Generate embeddings for large document collections efficiently"],"best_for":["Production systems processing 10+ concurrent requests","Batch evaluation on benchmark datasets (MMLU, HellaSwag, etc.)","High-throughput inference services with variable input lengths","Offline processing of large document collections"],"limitations":["Padding overhead increases with sequence length variance — worst case is 50% wasted computation if batch contains both 100-token and 4000-token sequences","Memory usage scales linearly with batch size and max sequence length — OOM errors likely with batch_size>32 on 16GB GPUs","Dynamic batching requires careful tuning of batch size and max_length parameters for optimal throughput","No built-in request queuing or load balancing — requires external orchestration for production deployments","Attention mask computation adds ~5-10% overhead per batch"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support (strongly recommended)","Transformers library 4.40+","GPU with 16GB+ VRAM for batch_size>16"],"input_types":["text (variable-length sequences)","numeric (batch_size, max_length parameters)"],"output_types":["numeric (logits for next token prediction)","numeric (hidden states/embeddings from final layer)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_3","uri":"capability://text.generation.language.zero.shot.and.few.shot.task.adaptation.through.prompt.engineering","name":"zero-shot and few-shot task adaptation through prompt engineering","description":"Adapts to new tasks without fine-tuning by conditioning generation on task-specific prompts or in-context examples. The model uses its instruction-following capabilities to interpret task descriptions and example input-output pairs, then generates outputs following the demonstrated pattern. This works through the transformer's ability to recognize patterns in the prompt and extrapolate them to new inputs, without any parameter updates.","intents":["Adapt the model to new tasks (classification, summarization, translation) without retraining","Provide few-shot examples to improve performance on domain-specific tasks","Test model behavior on novel tasks to understand generalization capabilities","Build flexible applications that handle multiple task types with a single model"],"best_for":["Rapid prototyping of new NLP tasks without fine-tuning infrastructure","Applications requiring multi-task support with a single model","Research exploring model generalization and in-context learning","Low-resource scenarios where fine-tuning data is unavailable"],"limitations":["Performance degrades significantly on tasks requiring specialized knowledge or complex reasoning — zero-shot accuracy on MMLU is ~40-50% vs 70%+ with fine-tuning","Few-shot learning is limited by context window size — typically 2-5 examples fit before context exhaustion","Prompt sensitivity is high — small wording changes can cause 10-20% accuracy swings","No mechanism for learning task-specific patterns beyond in-context examples — cannot adapt to domain-specific terminology","Hallucination risk increases with few-shot examples if examples are contradictory or noisy"],"requires":["Python 3.8+","Transformers library 4.40+","Carefully crafted prompt templates (no automatic generation)"],"input_types":["text (task description)","text (few-shot examples in input-output format)","text (new input to apply task to)"],"output_types":["text (task-specific output following demonstrated pattern)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_4","uri":"capability://text.generation.language.multilingual.text.generation.with.language.specific.tokenization","name":"multilingual text generation with language-specific tokenization","description":"Generates coherent text in multiple languages (Chinese, English, and others) using a shared vocabulary tokenizer that handles language-specific characters and subword units. The model's embedding layer and transformer layers are language-agnostic, allowing it to process and generate text across languages without language-specific branches. Language selection is implicit through the input text — the model detects language from input tokens and generates in the same language.","intents":["Build chatbots that support multiple languages without separate models","Generate responses in the user's native language automatically","Translate or code-switch between languages within a single conversation","Support global applications with minimal model overhead"],"best_for":["Global applications serving users in multiple language regions","Multilingual chatbot platforms requiring unified model deployment","Research on cross-lingual transfer and language generalization","Applications where language switching is frequent or unpredictable"],"limitations":["Performance varies significantly by language — Chinese and English are well-supported, but other languages may have degraded quality","Tokenizer efficiency differs by language — CJK languages require more tokens per character, increasing inference latency by 20-30%","No explicit language tagging — model must infer language from input, causing occasional code-switching errors","Training data distribution likely skewed toward English and Chinese — low-resource languages may underperform","No built-in language detection or explicit language control tokens"],"requires":["Python 3.8+","Transformers library 4.40+ with multilingual tokenizer support","UTF-8 encoding support in input pipeline"],"input_types":["text (any supported language: Chinese, English, etc.)"],"output_types":["text (output in same language as input)"],"categories":["text-generation-language","multilingual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_5","uri":"capability://text.generation.language.structured.output.generation.with.constrained.decoding","name":"structured output generation with constrained decoding","description":"Generates text that conforms to specified formats (JSON, XML, CSV) by constraining the token generation process to only produce valid tokens for the target format. The model uses grammar-based or regex-based constraints applied during sampling to filter invalid tokens before they are selected, ensuring output always matches the specified schema. This works by maintaining a state machine that tracks valid next tokens based on the format specification.","intents":["Extract structured data from text while ensuring valid JSON/XML output","Generate function arguments or API payloads in guaranteed valid formats","Create structured logs or reports with consistent formatting","Build reliable downstream processing pipelines that expect specific formats"],"best_for":["Applications requiring guaranteed valid JSON/XML output for downstream processing","Function calling or tool-use scenarios where argument format must be exact","Data extraction pipelines that cannot tolerate malformed output","Systems integrating with strict schema validation"],"limitations":["Constrained decoding adds 15-30% latency overhead due to token filtering at each step","Complex schemas (deeply nested JSON, large enums) may cause significant slowdown","Grammar constraints must be manually specified — no automatic schema inference","Model may struggle to generate valid output if training data lacked examples of the target format","Constraint violations are silently truncated — no error reporting for failed constraints"],"requires":["Python 3.8+","Transformers library 4.40+ with constrained generation support","Grammar specification (JSON schema, regex, or EBNF format)","Optional: outlines library for advanced grammar support"],"input_types":["text (prompt)","schema specification (JSON schema, regex, or grammar)"],"output_types":["text (guaranteed valid output matching schema)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_6","uri":"capability://text.generation.language.embedding.generation.for.semantic.similarity.and.retrieval","name":"embedding generation for semantic similarity and retrieval","description":"Extracts dense vector representations (embeddings) from the model's hidden states, typically from the final transformer layer, that capture semantic meaning of input text. These embeddings can be compared using cosine similarity or other distance metrics to find semantically similar documents or enable semantic search. The model produces fixed-dimensional vectors (typically 4096-8192 dimensions for a 4B model) that encode the meaning of the entire input sequence.","intents":["Build semantic search systems that find relevant documents by meaning rather than keywords","Cluster documents or user queries by semantic similarity","Create embedding-based recommendation systems","Enable similarity-based deduplication of documents or queries"],"best_for":["Semantic search and retrieval-augmented generation (RAG) systems","Document clustering and similarity analysis","Recommendation systems based on semantic similarity","Applications requiring fast similarity comparisons via vector databases"],"limitations":["Embedding quality depends on input length — longer sequences may have degraded semantic representation","No explicit embedding optimization during training — embeddings are byproduct of language modeling, not purpose-built","Embedding dimensionality is fixed at model's hidden size (~4096 for 4B model) — cannot reduce dimensions without quality loss","No built-in normalization or scaling — embeddings must be L2-normalized before similarity comparison","Embeddings are not optimized for any specific downstream task — may underperform vs task-specific embedding models"],"requires":["Python 3.8+","Transformers library 4.40+","Vector database or similarity library (e.g., FAISS, Pinecone, Weaviate)","Optional: scikit-learn for dimensionality reduction"],"input_types":["text (document or query)"],"output_types":["numeric (dense vector, typically 4096-8192 dimensions)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_7","uri":"capability://text.generation.language.context.window.management.with.sliding.window.attention","name":"context window management with sliding window attention","description":"Manages input sequences up to a fixed context window size (likely 4K-8K tokens) using standard transformer attention, where each token attends to all previous tokens within the window. The model uses position embeddings to encode absolute or relative token positions, enabling it to understand token order and distance relationships. When input exceeds context window, sequences are truncated or summarized externally — the model has no built-in mechanism for handling longer contexts.","intents":["Process documents or conversations up to the context window limit","Maintain multi-turn conversation history within a single inference call","Understand long-range dependencies within documents","Implement sliding window approaches for processing longer documents"],"best_for":["Conversational AI with moderate conversation history (10-20 turns)","Document analysis for documents up to 4K-8K tokens","Applications where context window size is known and manageable","Systems implementing external sliding window or summarization strategies"],"limitations":["Fixed context window size (likely 4K-8K tokens) — cannot process longer documents without external truncation or summarization","No built-in long-context handling mechanisms (e.g., sparse attention, retrieval augmentation) — must be implemented externally","Attention computation is O(n²) in sequence length — doubling context window quadruples memory and compute","Position embeddings may not extrapolate well beyond training context window — performance degrades for longer sequences","No mechanism for prioritizing important tokens — all tokens within window are treated equally"],"requires":["Python 3.8+","Transformers library 4.40+","External document chunking or summarization logic for longer documents"],"input_types":["text (up to context window size)"],"output_types":["text (generated response)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_8","uri":"capability://text.generation.language.safety.filtering.and.content.moderation.through.instruction.tuning","name":"safety filtering and content moderation through instruction-tuning","description":"Reduces generation of harmful, toxic, or inappropriate content through instruction-tuning on safety-aligned examples and rejection of unsafe prompts. The model learns to recognize unsafe requests and either refuse to respond or generate safe alternatives, without explicit safety classifiers or post-hoc filtering. Safety is embedded in the model's learned behavior rather than enforced through external guardrails.","intents":["Deploy models in production with reduced risk of harmful output","Refuse unsafe requests while maintaining helpful behavior for legitimate queries","Reduce need for external content moderation systems","Build applications compliant with content policies"],"best_for":["Production deployments requiring baseline safety without external moderation","Applications serving general audiences with content policies","Systems where external moderation is unavailable or too expensive","Research on instruction-tuning for safety alignment"],"limitations":["Safety is probabilistic — model may still generate harmful content on adversarial inputs or jailbreak attempts","No transparency into safety decision-making — cannot explain why a request was refused","Safety training may be biased toward certain types of harm while missing others","Adversarial users can potentially bypass safety through prompt engineering or indirect requests","Safety filtering may be overly conservative, refusing legitimate requests (false positives)","No built-in audit trail or logging of safety decisions for compliance"],"requires":["Python 3.8+","Transformers library 4.40+","Optional: external moderation APIs for additional safety layers"],"input_types":["text (user prompt)"],"output_types":["text (safe response or refusal)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__cap_9","uri":"capability://text.generation.language.efficient.inference.on.edge.devices.through.quantization.and.model.optimization","name":"efficient inference on edge devices through quantization and model optimization","description":"Supports quantized versions (INT8, INT4, or lower precision) that reduce model size and memory requirements while maintaining reasonable performance, enabling deployment on resource-constrained devices like mobile phones, edge servers, or embedded systems. Quantization reduces precision of weights and activations from 32-bit floats to lower bit widths, reducing memory footprint by 4-8x. The model architecture is optimized for inference efficiency through techniques like grouped query attention and flash attention.","intents":["Deploy the model on mobile devices or edge servers with limited memory","Reduce inference latency on CPU-only systems","Lower operational costs by reducing GPU memory requirements","Enable on-device inference for privacy-sensitive applications"],"best_for":["Mobile applications requiring on-device inference","Edge computing scenarios with limited GPU/memory","Privacy-critical applications avoiding cloud inference","Cost-sensitive deployments with many inference instances"],"limitations":["Quantization reduces model quality by 5-15% depending on bit width — INT4 quantization may cause noticeable degradation","Quantized inference requires specialized libraries (GGML, llama.cpp, or similar) — not all frameworks support all quantization formats","Quantization is typically applied post-training — no fine-tuning on quantized weights to recover quality","Memory savings are offset by slower inference on CPUs — quantized model on CPU may be slower than full-precision on GPU","Limited support for dynamic quantization — most quantization is static and requires recompilation for different batch sizes"],"requires":["Python 3.8+","Quantization library (e.g., bitsandbytes, GPTQ, or GGML)","Optional: llama.cpp or similar for CPU inference","For mobile: ONNX Runtime or TensorFlow Lite conversion"],"input_types":["text (prompt)"],"output_types":["text (generated response)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen3-4b-instruct-2507__headline","uri":"capability://text.generation.language.ai.text.generation.model.for.chatbots.and.assistants","name":"ai text generation model for chatbots and assistants","description":"Qwen3-4B-Instruct-2507 is a powerful AI text generation model specifically designed for creating conversational agents and chatbots, enabling seamless interaction and assistance in various applications.","intents":["best AI text generation model","text generation for chatbots","top conversational AI model","AI assistant for customer support","text generation for interactive applications"],"best_for":["chatbot development","customer service automation"],"limitations":["may require fine-tuning for specific domains"],"requires":["access to Hugging Face platform"],"input_types":["text prompts"],"output_types":["generated text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Transformers library 4.40+","Minimum 8GB RAM for inference (16GB recommended for batch processing)","CUDA 11.8+ for GPU acceleration (optional but strongly recommended)","Transformers library 4.40+ with streaming support","PyTorch 2.0+","Optional: CUDA 11.8+ for GPU acceleration","peft library (Parameter-Efficient Fine-Tuning) for LoRA support","GPU with 8GB+ VRAM for QLoRA, 16GB+ for standard LoRA"],"failure_modes":["4B parameter scale limits reasoning depth compared to 7B+ models — struggles with multi-step logical problems","Context window size not explicitly documented — likely 4K-8K tokens, limiting long document processing","No built-in retrieval augmentation — cannot access external knowledge bases or real-time information","Training data cutoff (likely 2024 or earlier) means no knowledge of recent events","Single-GPU inference recommended; multi-GPU scaling not optimized for models this size","Streaming adds ~50-100ms latency per token on CPU; GPU reduces to 10-30ms but requires CUDA setup","Temperature scaling is applied at sampling time — cannot retroactively adjust creativity of already-generated tokens","Top-k and top-p filtering reduce vocabulary diversity but may truncate valid low-probability tokens","No built-in beam search or diverse beam search — single-path generation only","Sampling strategies are stateless — no memory of previous samples for avoiding repetition","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9109792263107128,"quality":0.34,"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":10691206,"model_likes":829}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen--qwen3-4b-instruct-2507","compare_url":"https://unfragile.ai/compare?artifact=qwen--qwen3-4b-instruct-2507"}},"signature":"HMxyxDby4olWCah1OUwf1q4t90KNm+KfFtR1zSrbqMNTNXbeOyOHAqcyAsjAXmGZfxQZyUFFWrb8/OdO+6uxDw==","signedAt":"2026-06-23T16:09:41.150Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen--qwen3-4b-instruct-2507","artifact":"https://unfragile.ai/qwen--qwen3-4b-instruct-2507","verify":"https://unfragile.ai/api/v1/verify?slug=qwen--qwen3-4b-instruct-2507","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"}}