{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-neural-chat","slug":"neural-chat","name":"Neural Chat (7B)","type":"model","url":"https://ollama.com/library/neural-chat","page_url":"https://unfragile.ai/neural-chat","categories":["chatbots-assistants"],"tags":["ollama","open-source","intel"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-neural-chat__cap_0","uri":"capability://text.generation.language.conversational.text.generation.via.transformer","name":"conversational-text-generation-via-transformer","description":"Generates multi-turn conversational responses using a 7B-parameter Mistral-based transformer fine-tuned by Intel for dialogue. Processes text input through a 32K token context window and outputs coherent continuations via standard language modeling (next-token prediction). Deployed through Ollama's GGUF quantization format, enabling local inference without cloud dependencies. Supports streaming output and role-based message formatting (user/assistant/system).","intents":["Build a local chatbot that runs entirely on-device without API calls","Integrate a conversation model into an application with low latency requirements","Deploy a text-generation backend that respects user privacy by avoiding cloud transmission","Create a multi-turn dialogue system with 32K token context for longer conversations"],"best_for":["Solo developers building privacy-first chatbot applications","Teams deploying LLM inference on-premises or edge devices","Builders prototyping conversational AI without cloud API costs","Organizations with strict data residency requirements"],"limitations":["No benchmark data provided — actual MMLU/HellaSwag performance unknown, making quality comparison to alternatives impossible","32K token context is fixed and cannot be extended; insufficient for very long document analysis or multi-document reasoning","Model last updated 2 years ago — may lack knowledge of recent events and may underperform vs. newer models like Llama 2 or Mistral 8x7B","Fine-tuning methodology and dataset composition undocumented — unclear what conversational patterns were optimized for","No explicit language or domain coverage specification despite claims of 'good coverage' — actual multilingual or specialized domain performance unknown","Inference speed and hardware requirements not specified — no TTFT (time-to-first-token) or throughput benchmarks provided"],"requires":["Ollama runtime (any recent version supporting GGUF format)","4.1GB disk space for quantized model weights","Sufficient RAM for model loading (estimated 8GB+ for comfortable inference, exact requirement unknown)","Python 3.8+ or Node.js 14+ for SDK integration (if using language bindings)","Optional: GPU with CUDA/Metal support for accelerated inference (CPU-only inference feasible but speed unknown)"],"input_types":["text (plain string)","structured chat messages (JSON with role/content fields: {\"role\": \"user\", \"content\": \"...\"})"],"output_types":["text (streaming or complete)","structured chat completion response (JSON with model, message, and metadata)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_1","uri":"capability://automation.workflow.local.inference.via.ollama.gguf.quantization","name":"local-inference-via-ollama-gguf-quantization","description":"Executes model inference entirely on local hardware using Ollama's GGUF quantization format, which compresses the 7B transformer into a 4.1GB binary optimized for CPU and GPU inference. Ollama abstracts hardware acceleration (CUDA, Metal, ROCm) and provides HTTP API endpoints (localhost:11434/api/chat) and CLI access without requiring manual VRAM management or model compilation. Supports streaming responses and concurrent requests through Ollama's runtime scheduler.","intents":["Run a language model on a laptop or server without cloud API dependencies","Avoid per-token API costs by self-hosting inference","Ensure data never leaves the local network or device","Integrate model inference into applications via simple HTTP API calls"],"best_for":["Privacy-conscious developers building applications with sensitive data","Cost-optimized teams running high-volume inference workloads","Edge computing scenarios (on-device inference for mobile/IoT)","Organizations with air-gapped or offline-first requirements"],"limitations":["Inference speed and throughput not benchmarked — actual tokens-per-second performance unknown, making latency predictions impossible","Hardware acceleration support depends on Ollama version and local GPU drivers — CUDA/Metal/ROCm compatibility not guaranteed across all systems","GGUF quantization level unknown — actual precision loss and quality degradation vs. original FP32 weights unspecified","No multi-GPU or distributed inference support documented — limited to single-machine deployment","Ollama runtime adds abstraction overhead — exact latency penalty vs. raw inference unknown","No persistent caching or KV-cache optimization details provided — each inference request may recompute context"],"requires":["Ollama runtime installed (any recent version with GGUF support)","4.1GB available disk space for model download and storage","Sufficient RAM (estimated 8GB+ for inference, exact requirement unknown)","Optional: NVIDIA GPU with CUDA 11.8+ or Apple Silicon with Metal support for acceleration","Network access to Ollama API endpoint (localhost:11434 by default, configurable)"],"input_types":["text (plain string via HTTP POST)","structured JSON chat messages (role/content format)"],"output_types":["text (streaming via Server-Sent Events or complete response)","JSON response with model metadata, completion tokens, and timing info"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_10","uri":"capability://automation.workflow.open.source.model.weights.and.reproducibility","name":"open-source-model-weights-and-reproducibility","description":"Model weights are publicly available on HuggingFace (Intel/neural-chat-7b-v3-1) under an open-source license, enabling full reproducibility, fine-tuning, and modification. Unlike proprietary cloud models, the complete model can be downloaded, inspected, and deployed without vendor lock-in. Ollama's GGUF distribution is derived from these open weights, maintaining full transparency and enabling users to verify model integrity.","intents":["Inspect and understand model architecture and weights without black-box constraints","Fine-tune the model on custom datasets for domain-specific applications","Deploy the model in air-gapped or offline environments without cloud dependencies","Avoid vendor lock-in by using an open-source model that can be deployed anywhere"],"best_for":["Researchers and academics studying model behavior and fine-tuning","Organizations with strict data residency or vendor independence requirements","Teams building proprietary applications that cannot depend on cloud APIs","Developers wanting full control over model deployment and updates"],"limitations":["License terms not specified in Ollama documentation — must consult HuggingFace model card for license details and commercial use restrictions","No guarantee of model stability or maintenance — Intel may discontinue support or updates at any time","Open weights enable misuse (e.g., fine-tuning for harmful purposes) — no built-in safeguards or usage restrictions","Fine-tuning from scratch requires significant compute resources and expertise — not accessible to all users","Model card and documentation on HuggingFace may be incomplete or outdated — no guarantee of accuracy or completeness","No official support or SLA — users must rely on community forums or self-support"],"requires":["HuggingFace account (optional, for downloading weights)","Sufficient disk space for model weights (4.1GB for GGUF, ~14GB for full-precision)","Optional: GPU and training framework (PyTorch, etc.) for fine-tuning"],"input_types":["Model weights (PyTorch, GGUF, or other formats)"],"output_types":["Fine-tuned model weights or inference results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_2","uri":"capability://memory.knowledge.multi.turn.dialogue.context.management","name":"multi-turn-dialogue-context-management","description":"Maintains conversation state across multiple turns by accepting a message history array (role/content pairs) and processing the full context window (up to 32K tokens) to generate contextually-aware responses. The model attends to all prior messages in the conversation, enabling coherent follow-ups, reference resolution, and topic continuity. Ollama's API handles message serialization and context windowing — when total tokens exceed 32K, behavior is undefined (likely truncation or error, not documented).","intents":["Build a chatbot that remembers previous messages and maintains conversation coherence","Enable users to ask follow-up questions that reference earlier parts of the conversation","Create a dialogue system where the model can resolve pronouns and implicit references","Support long-form conversations without resetting context between turns"],"best_for":["Developers building conversational interfaces (chatbots, customer support, tutoring systems)","Teams creating multi-turn dialogue datasets or evaluation benchmarks","Applications requiring contextual understanding within a single session"],"limitations":["32K token context window is fixed and cannot be extended — conversations exceeding this limit will lose earlier context (truncation behavior not documented)","No explicit memory or persistent context storage — each API call must include full message history; no server-side session management","Context window management is manual — developers must track token counts and decide what to include/exclude when approaching 32K limit","No fine-grained control over context weighting — all messages treated equally; no mechanism to prioritize recent vs. early messages","Streaming output may break context coherence if interrupted mid-response — no resumption or recovery mechanism documented","No explicit handling of context overflow — behavior when exceeding 32K tokens (truncation, error, or silent loss) is undefined"],"requires":["Ollama runtime with neural-chat model loaded","Message history formatted as JSON array: [{\"role\": \"user\", \"content\": \"...\"}, {\"role\": \"assistant\", \"content\": \"...\"}]","Token counting utility to track context usage (not provided by Ollama; developers must implement or use external library)","Application-level session management to persist and retrieve message history between API calls"],"input_types":["JSON array of message objects with 'role' (user/assistant/system) and 'content' (text) fields"],"output_types":["text (single assistant message)","JSON response including the generated message and token usage metadata"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_3","uri":"capability://text.generation.language.streaming.token.output.for.real.time.ux","name":"streaming-token-output-for-real-time-ux","description":"Outputs generated tokens incrementally via Server-Sent Events (SSE) streaming, allowing real-time display of model output as it is generated rather than waiting for the complete response. Ollama's HTTP API supports streaming mode (stream=true parameter) which yields newline-delimited JSON objects, each containing a single token or partial response chunk. This enables responsive user interfaces where text appears character-by-character, improving perceived latency and user experience.","intents":["Display model output in real-time as it is generated, improving perceived responsiveness","Build interactive chat interfaces where users see tokens appearing live","Reduce perceived latency by showing partial results while inference completes","Implement cancellation/interruption of long-running generations mid-stream"],"best_for":["Web and mobile application developers building chat UIs","Teams creating interactive AI assistants with real-time feedback","Builders optimizing for perceived latency and user engagement"],"limitations":["Streaming behavior and token chunking strategy not documented — unclear whether each JSON object contains one token or multiple tokens, affecting UI rendering granularity","No built-in cancellation mechanism — interrupting a stream requires closing the HTTP connection; no graceful stop-token or abort signal documented","Streaming adds complexity to error handling — errors mid-stream may not be properly propagated to the client, leaving partial responses in the UI","Token-by-token streaming may expose model internals (e.g., reasoning tokens, formatting artifacts) that degrade UX if not filtered","No backpressure handling documented — fast clients may overwhelm with requests if streaming is not rate-limited","Streaming latency overhead unknown — actual TTFT (time-to-first-token) and inter-token latency not benchmarked"],"requires":["HTTP client supporting Server-Sent Events (SSE) or streaming HTTP responses","Ollama API endpoint with stream=true parameter in request","Frontend capable of parsing newline-delimited JSON and rendering incremental text updates","Optional: JavaScript fetch API with ReadableStream support, or Python requests library with stream=True"],"input_types":["JSON request with stream=true flag and standard chat message format"],"output_types":["Server-Sent Events (SSE) stream of newline-delimited JSON objects, each containing partial response data"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_4","uri":"capability://tool.use.integration.http.api.integration.for.polyglot.applications","name":"http-api-integration-for-polyglot-applications","description":"Exposes model inference through a standard HTTP REST API (localhost:11434/api/chat) that accepts JSON requests and returns JSON responses, enabling integration from any programming language or framework without language-specific SDKs. Ollama provides official Python and JavaScript libraries as convenience wrappers, but the underlying HTTP API is language-agnostic and can be called via cURL, HTTP clients, or custom code. API supports both streaming and non-streaming modes, with configurable parameters (temperature, top_p, etc.).","intents":["Integrate model inference into applications written in languages without official Ollama SDKs","Build polyglot systems where different services call the same inference endpoint","Enable inference from shell scripts, CI/CD pipelines, or command-line tools via cURL","Decouple inference infrastructure from application code, allowing model updates without code changes"],"best_for":["Polyglot development teams using multiple programming languages","DevOps and infrastructure teams deploying inference as a shared service","Builders integrating inference into existing systems via HTTP","Teams using infrastructure-as-code or containerized deployments"],"limitations":["HTTP overhead adds latency vs. in-process inference — exact penalty unknown, but network round-trip and JSON serialization add measurable delay","API parameter documentation incomplete — supported parameters (temperature, top_p, top_k, repeat_penalty, etc.) and their effects not fully documented","No authentication or authorization built into Ollama HTTP API — requires external reverse proxy (nginx, Envoy) for security; localhost-only by default","No rate limiting or quota management in Ollama — requires external middleware for multi-tenant scenarios","Response format and error handling not fully specified — error codes, error message structure, and edge case behavior undefined","No built-in request/response logging or observability — requires external monitoring tools to track API usage and performance"],"requires":["Ollama runtime running and listening on localhost:11434 (or configured remote endpoint)","HTTP client library (curl, requests, fetch, etc.)","JSON serialization/deserialization capability in the calling language","Network connectivity to Ollama endpoint (localhost or remote)"],"input_types":["JSON POST request with model name, messages array, and optional parameters (temperature, top_p, etc.)"],"output_types":["JSON response with generated message, model metadata, and token usage statistics","Streaming: newline-delimited JSON objects (SSE format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_5","uri":"capability://automation.workflow.cli.based.inference.for.scripting.and.automation","name":"cli-based-inference-for-scripting-and-automation","description":"Provides command-line interface (ollama run neural-chat) for invoking model inference directly from shell scripts, CI/CD pipelines, or interactive terminal sessions. CLI accepts text input via stdin or command-line arguments and outputs generated text to stdout, enabling integration into Unix pipelines and automation workflows. Supports interactive multi-turn conversations in the terminal without requiring HTTP client setup or JSON formatting.","intents":["Test model behavior quickly from the command line without writing code","Integrate model inference into shell scripts and automation workflows","Use model output in Unix pipelines (piping to grep, sed, awk, etc.)","Build CI/CD pipeline steps that call the model for code generation, testing, or documentation"],"best_for":["DevOps engineers and system administrators automating infrastructure tasks","Developers prototyping and testing model behavior interactively","Teams building shell-based automation and scripting workflows","Builders integrating inference into existing command-line tools"],"limitations":["CLI interface design and options not documented — unclear what flags/parameters are supported (temperature, top_p, context length, etc.)","Interactive mode behavior undefined — how multi-turn conversations are managed in the terminal, how to exit, how to clear context not specified","No structured output format (JSON, YAML) documented for CLI — output is plain text, making parsing in scripts fragile and error-prone","Streaming behavior in CLI not documented — unclear if tokens appear in real-time or buffered","No built-in timeout or resource limits — long-running generations may hang indefinitely","Error handling and exit codes not specified — difficult to detect failures in automated scripts"],"requires":["Ollama runtime installed and in system PATH","Bash or compatible shell for scripting","Optional: jq or other JSON parser if structured output is needed (though not documented as available)"],"input_types":["plain text via stdin or command-line arguments"],"output_types":["plain text to stdout"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_6","uri":"capability://tool.use.integration.sdk.bindings.for.python.and.javascript","name":"sdk-bindings-for-python-and-javascript","description":"Provides official Python and JavaScript/Node.js libraries that wrap Ollama's HTTP API, offering language-native abstractions for model inference. Libraries handle JSON serialization, HTTP client setup, and streaming response parsing, reducing boilerplate code. Python library integrates with popular frameworks (LangChain, LlamaIndex) via standard interfaces, enabling use in larger AI application stacks.","intents":["Call Ollama inference from Python or JavaScript without manually constructing HTTP requests","Integrate Ollama into LangChain or LlamaIndex applications via standard LLM provider interfaces","Handle streaming responses with language-native async/await or generator patterns","Reduce boilerplate code for common inference patterns (chat, completion, embeddings)"],"best_for":["Python developers building LLM applications with LangChain or LlamaIndex","Node.js/JavaScript developers integrating inference into web applications","Teams standardizing on Python or JavaScript for AI application development"],"limitations":["SDK documentation and API surface not provided — unclear what methods, parameters, and options are available","LangChain/LlamaIndex integration details unknown — unclear what interfaces are implemented (LLM, ChatModel, Embeddings, etc.)","Async/await support in JavaScript SDK not documented — unclear if streaming is async-native or callback-based","Python SDK version compatibility not specified — unclear what Python versions are supported","Error handling and exception types not documented — difficult to write robust error handling code","No type hints or IDE autocomplete support documented for Python SDK"],"requires":["Python 3.8+ (estimated, not specified) or Node.js 14+ (estimated, not specified)","Ollama runtime running and accessible","Optional: LangChain or LlamaIndex for framework integration"],"input_types":["Python: method calls with string or message list arguments","JavaScript: method calls with string or message array arguments"],"output_types":["Python: string or async generator for streaming","JavaScript: Promise<string> or async iterable for streaming"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_7","uri":"capability://automation.workflow.quantized.model.distribution.via.gguf.format","name":"quantized-model-distribution-via-gguf-format","description":"Model is distributed as a GGUF-format binary (4.1GB) optimized for inference on consumer hardware, rather than as raw PyTorch or ONNX weights. GGUF quantization compresses the 7B transformer to a fraction of its original size, enabling inference on devices with limited VRAM (estimated 8GB+ RAM sufficient, exact requirement unknown). Ollama handles GGUF loading, memory mapping, and hardware acceleration abstraction, requiring no manual model compilation or format conversion.","intents":["Run a 7B model on consumer laptops and small servers without high-end GPUs","Reduce model download size and storage footprint for faster distribution","Enable inference on edge devices and resource-constrained environments","Avoid manual model compilation and format conversion workflows"],"best_for":["Individual developers and small teams with limited hardware budgets","Edge computing and on-device inference scenarios","Organizations distributing models to end users (e.g., desktop applications)","Teams prioritizing ease of deployment over inference performance"],"limitations":["Quantization level and precision loss unknown — GGUF format supports multiple quantization levels (Q4, Q5, Q8), but Neural Chat's specific level not documented, making quality comparison impossible","Quantization-induced quality degradation not benchmarked — no data on how quantization affects model accuracy, reasoning, or output quality","GGUF format is Ollama-specific — model cannot be easily converted to other formats (ONNX, TensorRT) without additional tooling","Memory requirements not specified — actual RAM usage during inference depends on quantization level and batch size, both unknown","Inference speed not benchmarked — tokens-per-second performance on various hardware (CPU, GPU) unknown, making latency predictions impossible","No option to use full-precision weights — GGUF quantization is mandatory, no FP32 or FP16 alternative provided"],"requires":["4.1GB disk space for model download and storage","Estimated 8GB+ RAM for inference (exact requirement unknown)","Ollama runtime with GGUF support","Optional: GPU with CUDA/Metal support for acceleration (CPU-only inference feasible but slow)"],"input_types":["GGUF binary file (downloaded automatically by Ollama)"],"output_types":["Inference results (text) via Ollama API"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_8","uri":"capability://text.generation.language.conversation.focused.fine.tuning.optimization","name":"conversation-focused-fine-tuning-optimization","description":"Model is fine-tuned specifically for conversational tasks (dialogue, multi-turn interactions) rather than general-purpose text generation. Fine-tuning approach, dataset, and optimization objectives are undocumented, but the model is positioned as conversation-optimized compared to base Mistral. This specialization may improve dialogue coherence, instruction-following, and turn-taking behavior, though no benchmarks validate these claims.","intents":["Deploy a model optimized for chatbot and dialogue applications without fine-tuning from scratch","Improve conversation quality and coherence compared to base language models","Reduce fine-tuning effort by starting with a conversation-optimized checkpoint","Build dialogue systems that handle multi-turn interactions more naturally"],"best_for":["Teams building chatbot and dialogue systems without resources for custom fine-tuning","Developers seeking conversation-optimized models without cloud API dependencies","Organizations deploying conversational AI on-premises"],"limitations":["Fine-tuning methodology completely undocumented — unclear what techniques were used (SFT, DPO, RLHF, etc.), making it impossible to assess optimization quality","Fine-tuning dataset not disclosed — no information on data sources, size, quality, or composition; unclear what conversational patterns were optimized for","No benchmark data comparing Neural Chat to base Mistral or other conversation models — claims of conversation optimization are unvalidated","Optimization objectives unknown — unclear whether model was optimized for instruction-following, coherence, safety, or other dimensions","No evaluation on standard dialogue benchmarks (e.g., BLEU, ROUGE, human evaluation) — quality claims are purely marketing","Fine-tuning may introduce biases or limitations not present in base model — no documentation of known failure modes or biases"],"requires":["Ollama runtime with neural-chat model loaded","No special requirements — fine-tuning is baked into the model weights"],"input_types":["text (conversational input)"],"output_types":["text (conversational output)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-neural-chat__cap_9","uri":"capability://memory.knowledge.32k.token.context.window.for.long.conversations","name":"32k-token-context-window-for-long-conversations","description":"Supports a 32,000-token context window, enabling the model to process and respond to conversations or documents up to approximately 24,000 words (assuming ~1.3 tokens per word). This is substantially larger than the base Mistral 7B model (8K tokens) and many other 7B models, allowing longer multi-turn dialogues, document summarization, and reasoning over extended text without truncation or context loss.","intents":["Build chatbots that maintain coherent conversations over 50+ turns without losing context","Summarize or analyze documents longer than 8K tokens without chunking","Enable reasoning over multiple documents or long code files in a single context","Support longer user instructions and examples without context overflow"],"best_for":["Applications requiring long-form dialogue or document analysis","Teams building research assistants or code analysis tools","Builders creating systems that reason over multiple documents"],"limitations":["Context window is fixed at 32K tokens — cannot be extended via fine-tuning or configuration","Actual usable context may be less than 32K due to prompt overhead and model behavior — exact usable window unknown","Inference latency scales with context length — processing 32K tokens is significantly slower than 8K, exact penalty unknown","Memory requirements scale with context — 32K context requires more VRAM than 8K, exact scaling factor unknown","No documentation on how model handles context overflow — behavior when exceeding 32K tokens (truncation, error, or silent loss) undefined","Attention mechanism may not effectively use full context — transformer models sometimes ignore distant tokens; no analysis of effective context length provided"],"requires":["Ollama runtime with neural-chat model loaded","Sufficient RAM to hold 32K token context in memory (estimated 8GB+, exact requirement unknown)","Optional: GPU with sufficient VRAM for faster inference (exact requirement unknown)"],"input_types":["text (up to 32K tokens total, including conversation history)"],"output_types":["text (generated response)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any recent version supporting GGUF format)","4.1GB disk space for quantized model weights","Sufficient RAM for model loading (estimated 8GB+ for comfortable inference, exact requirement unknown)","Python 3.8+ or Node.js 14+ for SDK integration (if using language bindings)","Optional: GPU with CUDA/Metal support for accelerated inference (CPU-only inference feasible but speed unknown)","Ollama runtime installed (any recent version with GGUF support)","4.1GB available disk space for model download and storage","Sufficient RAM (estimated 8GB+ for inference, exact requirement unknown)","Optional: NVIDIA GPU with CUDA 11.8+ or Apple Silicon with Metal support for acceleration","Network access to Ollama API endpoint (localhost:11434 by default, configurable)"],"failure_modes":["No benchmark data provided — actual MMLU/HellaSwag performance unknown, making quality comparison to alternatives impossible","32K token context is fixed and cannot be extended; insufficient for very long document analysis or multi-document reasoning","Model last updated 2 years ago — may lack knowledge of recent events and may underperform vs. newer models like Llama 2 or Mistral 8x7B","Fine-tuning methodology and dataset composition undocumented — unclear what conversational patterns were optimized for","No explicit language or domain coverage specification despite claims of 'good coverage' — actual multilingual or specialized domain performance unknown","Inference speed and hardware requirements not specified — no TTFT (time-to-first-token) or throughput benchmarks provided","Inference speed and throughput not benchmarked — actual tokens-per-second performance unknown, making latency predictions impossible","Hardware acceleration support depends on Ollama version and local GPU drivers — CUDA/Metal/ROCm compatibility not guaranteed across all systems","GGUF quantization level unknown — actual precision loss and quality degradation vs. original FP32 weights unspecified","No multi-GPU or distributed inference support documented — limited to single-machine deployment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.38999999999999996,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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=neural-chat","compare_url":"https://unfragile.ai/compare?artifact=neural-chat"}},"signature":"oCJHW8bWWrhJDTZXyD407wkOqBOAlm4XTG7ZiWijODCeKvVPOGrF0nbv0IvoPD76kyKUDGhSjM1lCT9iJCsyCA==","signedAt":"2026-06-22T12:08:02.594Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/neural-chat","artifact":"https://unfragile.ai/neural-chat","verify":"https://unfragile.ai/api/v1/verify?slug=neural-chat","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"}}