{ "data": { "a": { "slug": "kronos", "name": "Kronos", "vendor": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "vendorUrl": "https://github.com/shiyu-coder/Kronos", "kind": "weights", "category": "open-weights", "summary": "Kronos is an open-weight family of small forecasting models for financial candlestick (K-line) series, from Tsinghua University researchers. It reads open, high, low, close, volume and amount bars with timestamps and generates forecast bars, under MIT.", "url": "https://www.anchorterminal.com/tools/kronos", "markdownUrl": "https://www.anchorterminal.com/tools/kronos.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kronos.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kronos.json", "dns": "kronos.grade.anchorterminal.com", "repo": "https://github.com/shiyu-coder/Kronos", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "NeoQuasar/Kronos-mini" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-small" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-base" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-Tokenizer-base" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-Tokenizer-2k" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for all five NeoQuasar repositories, and the code is in a public GitHub repository (checked 2026-10-25).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in each card's metadata and in the GitHub repository's LICENSE. Nobody sells per-call access and no inference provider serves it. Running it costs the hardware, 0.03 GB for Kronos-mini with its tokeniser and 0.43 GB for Kronos-base with its tokeniser, in FP32 (checked 2026-10-25).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-25).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 43000, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-25" }, "docsUrl": "https://github.com/shiyu-coder/Kronos", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "time-series", "forecasting", "finance", "candlestick", "k-line", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-04-13", "graded": true, "anchor": { "graded": true, "score": 55.2, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 91, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 55, "risk": 50, "upkeep": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.", "bestFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.", "strengths": [ "MIT with nothing added, ungated, safetensors only with SHA-256 hashes", "Under 0.5 GB for every public size, so it runs on a CPU, Apple MPS or a small GPU", "Input and output documented as plain DataFrames, with batch prediction over many series", "Training data listed by exchange with observation counts, filtering and a June 2024 end" ], "weaknesses": [ "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build", "A 512-bar context for the small and base sizes, and the mean forecast only, without quantiles", "The sample data the quickstart reads was deleted on 13 April 2026, and the cards' licence link points at a missing branch", "No safety results, no security contact and no publisher reply in its Hugging Face discussions since November 2025", "The publisher's forecasting results come from its own paper, with no independent benchmark found" ], "agentNotes": [ "Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor", "Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing", "The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars", "Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them", "Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths", "Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.2 } ], "editorialScores": { "interface": 60, "reach": 55, "risk": 50, "upkeep": 61 }, "provenanceScore": 50, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos\npip install -r requirements.txt # Python 3.10+, PyTorch 2.0+\npython - \u003c\u003c'EOF'\nimport pandas as pd\nfrom model import Kronos, KronosTokenizer, KronosPredictor\ntok = KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")\nmodel = Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")\np = KronosPredictor(model, tok, max_context=512) # picks cuda, mps or cpu\ndf = pd.read_csv(\"tests/data/regression_input.csv\", parse_dates=[\"timestamps\"])\nx, y = df.iloc[:400], df[\"timestamps\"].iloc[400:520]\ncols = [\"open\", \"high\", \"low\", \"close\", \"volume\", \"amount\"]\nprint(p.predict(df=x[cols], x_timestamp=x[\"timestamps\"], y_timestamp=y, pred_len=120, T=1.0, top_p=0.9, sample_count=1).head())\nEOF" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kronos" }, "weights": { "trainedBy": "Yu Shi and co-authors, Tsinghua University", "params": "4.1M to 102.3M", "context": 512, "goodFor": "Forecasting the next bars of a financial candlestick series, or sampling synthetic ones, on any machine, from a short history of open, high, low, close, volume and amount.", "runtimes": [], "builds": [ { "name": "Kronos-mini", "url": "https://huggingface.co/NeoQuasar/Kronos-mini", "who": "publisher", "precision": "fp32", "sizeGB": 0.03, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "32,304,418 bytes with Kronos-Tokenizer-2k, which it needs (16,451,361 for the model repository and 15,853,057 for the tokeniser). 2,048-bar context per the card" }, { "name": "Kronos-small", "url": "https://huggingface.co/NeoQuasar/Kronos-small", "who": "publisher", "precision": "fp32", "sizeGB": 0.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "114,844,309 bytes with Kronos-Tokenizer-base (98,991,258 and 15,853,051). The model the card's and README's examples load" }, { "name": "Kronos-base", "url": "https://huggingface.co/NeoQuasar/Kronos-base", "who": "publisher", "precision": "fp32", "sizeGB": 0.43, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "425,127,647 bytes with Kronos-Tokenizer-base (409,274,596 and 15,853,051). The largest public size" } ], "run": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos \u0026\u0026 pip install -r requirements.txt, then in Python: KronosPredictor(Kronos.from_pretrained(\"NeoQuasar/Kronos-small\"), KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\"), max_context=512).predict(df=..., x_timestamp=..., y_timestamp=..., pred_len=120)", "runBuild": "Kronos-small", "checked": "2026-10-25", "minMemoryGB": 8 }, "area": "models", "provenance": { "legalEntity": "No company. Yu Shi publishes it from the GitHub account shiyu-coder and the Hugging Face user account NeoQuasar (named ShiYu), with co-authors at Tsinghua University per the paper", "domain": "github.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-25", "notes": [ "NeoQuasar is a Hugging Face user account (fullname ShiYu), not an organisation. The GitHub README and the paper link it, so it's the publisher's own account.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "No SECURITY.md, security contact or disclosure policy was found in the GitHub repository or on the cards.", "No changelog and no GitHub releases. The README's News section has three dated items, the last on 10 November 2025." ], "score": 50 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kronos.json", "live": { "slug": "kronos", "githubStars": 47440, "securityTxt": { "url": "https://github.com/.well-known/security.txt", "state": "valid", "expires": "2026-12-10T16:07:34z", "checkedAt": "2026-11-10T16:07:34.352094479Z" }, "domain": { "domain": "github.com", "registered": "2007-10-09", "source": "https://rdap.verisign.com/com/v1/domain/github.com", "checkedAt": "2026-11-08T15:13:35.342098516Z" }, "github": { "repo": "shiyu-coder/kronos", "checkedAt": "2026-11-09T10:02:46.822302547Z", "stars": 47378, "archived": false, "issuesEnabled": true, "pushedAt": "2026-04-13T12:38:49Z", "openIssues": 221, "openPRs": 77, "issuesOpened90d": 17, "issuesClosed90d": 3, "prsMerged90d": 0, "replies": { "sampled": 25, "unanswered": 21, "medianDays": 0.5 } }, "updatedAt": "2026-11-10T16:07:34.352094479Z" } }, "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.", "b": { "slug": "nanbeige-4-2-3b", "name": "Nanbeige4.2-3B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json", "dns": "nanbeige-4-2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-FP8" }, { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "small", "looped-transformer", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "fp8", "custom-code", "self-hosted" ], "lastRelease": "2026-09-11", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 62, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.", "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU", "strengths": [ "Trained for multi-step tool use, with parsers in released SGLang", "A 256K context in a model that fits 8 GB", "Apache 2.0, ungated", "FP8 and 8-bit builds from Nanbeige", "Nanbeige replied in most of the recent discussions" ], "weaknesses": [ "Its own architecture: Transformers loads it only with the repository's code", "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion", "The card's serve instructions still point at Nanbeige's forks", "No safety results on the card", "Training data described only in general terms", "Not changed since September" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`", "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`", "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat", "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card", "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's", "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 72 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-2-3b" }, "sameCompany": [ "nanbeige-4-3-33b" ], "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata", "params": "4.2B", "context": 262144, "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Nanbeige4.2-3B-FP8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's FP8 build" }, { "name": "Nanbeige4.2-3B-GPTQ-Int8", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8", "who": "publisher", "precision": "int8", "sizeGB": 5.19, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Nanbeige's 8-bit GPTQ build" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 2.68, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026" }, { "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 4.43, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026" }, { "name": "Nanbeige4.2-3B (BF16 safetensors)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B", "who": "publisher", "precision": "bf16", "sizeGB": 8.34, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Two shards" } ], "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige", "runBuild": "Nanbeige4.2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.2", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.2-3B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 29 commits from 21 July to 11 September 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json" }, "edges": [ { "by": 30, "category": "reach", "leads": "nanbeige-4-2-3b", "name": "Reach", "reasons": { "a": "Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).", "b": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85." }, "scores": { "a": 55, "b": 85 } }, { "by": 25, "category": "interface", "leads": "nanbeige-4-2-3b", "name": "Agent interface", "reasons": { "a": "Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini).", "b": "Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 60, "b": 85 } }, { "by": 11, "category": "upkeep", "leads": "nanbeige-4-2-3b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.", "b": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72." }, "scores": { "a": 61, "b": 72 } }, { "by": 5, "category": "risk", "leads": "nanbeige-4-2-3b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "b": "Nanbeige LLM Lab (BOSS Zhipin)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-13", "b": "2026-09-11", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "43k stars", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.", "question": "Which is better for AI agents, Kronos or Nanbeige4.2-3B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.", "slug": "kronos", "watchFor": "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build" }, { "aheadOn": [ "Reach, 85 against 55", "Risk, 55 against 50", "Agent interface, 85 against 60", "Documentation and upkeep, 72 against 61" ], "also": null, "goodFor": "Local agents 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}, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b.json", "title": "Kronos vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b.json", "title": "Kronos vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-omni-2b.json", "title": "Kronos vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.json", "title": "Kronos vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-0-tiny.json", "title": "Kronos vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-0-tiny" }, { "json": 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"https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2.json", "title": "Nanbeige4.2-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json", "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b.json", "title": "Nanbeige4.2-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "nanbeige-4-2-3b", "key": "reach", "kronos": 55, "name": "Reach", "nanbeige-4-2-3b": 85, "weight": 25 }, { "by": 5, "edge": "nanbeige-4-2-3b", "key": "risk", "kronos": 50, "name": "Risk", "nanbeige-4-2-3b": 55, "weight": 20 }, { "by": 25, "edge": "nanbeige-4-2-3b", "key": "interface", "kronos": 60, "name": "Agent interface", "nanbeige-4-2-3b": 85, "weight": 10 }, { "by": 11, "edge": "nanbeige-4-2-3b", "key": "upkeep", "kronos": 61, "name": "Documentation and upkeep", "nanbeige-4-2-3b": 72, "weight": 10 } ], "summary": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "kronos": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.", "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b", "json": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.min.md" }, "markdown": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Kronos: grade C, 55.2/100, rank #122 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kronos.md · JSON https://www.anchorterminal.com/api/v1/tools/kronos.json\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kronos (C)\n\nGood for: Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.\n\nWatch for: Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build\n\n### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Reach, 85 against 55\n- Risk, 55 against 50\n- Agent interface, 85 against 60\n- Documentation and upkeep, 72 against 61\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n\n## Why the edge\n\n### Reach: Nanbeige4.2-3B +30\n\n- Kronos (55): Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n\n### Agent interface: Nanbeige4.2-3B +25\n\n- Kronos (60): Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini).\n- Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Documentation and upkeep: Nanbeige4.2-3B +11\n\n- Kronos (61): Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n\n### Risk: Nanbeige4.2-3B +5\n\n- Kronos (50): File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | Kronos | Nanbeige4.2-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 85 | Nanbeige4.2-3B +30 |\n| Risk | 20% (20 this run) | 50 | 55 | Nanbeige4.2-3B +5 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Nanbeige4.2-3B +25 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 72 | Nanbeige4.2-3B +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kronos | Nanbeige4.2-3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | Nanbeige LLM Lab (BOSS Zhipin) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-13 | 2026-09-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Popularity | 43k stars | none |\n\n## Verdicts\n\n**Kronos.** Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n## Before you call either\n\n### Kronos\n\n1. Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor\n2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing\n3. The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars\n4. Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them\n5. Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths\n6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n## Questions\n\n### Which is better for AI agents, Kronos or Nanbeige4.2-3B?\n\nNanbeige4.2-3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"nanbeige-4-2-3b\"}`. 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