{ "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": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and documentation and upkeep.", "b": { "slug": "step-3-7-flash", "name": "Step 3.7 Flash", "vendor": "StepFun", "vendorUrl": "https://platform.stepfun.ai", "kind": "weights", "category": "open-weights", "summary": "Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds.", "url": "https://www.anchorterminal.com/tools/step-3-7-flash", "markdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json", "dns": "step-3-7-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "license": "Apache-2.0 (metadata and card; no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-GGUF" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-FP8" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (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/stepfun-ai/Step-3.7-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "custom-code", "self-hosted" ], "lastRelease": "2026-06-03", "graded": true, "anchor": { "graded": true, "score": 59.8, "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": 73, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.", "bestFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "strengths": [ "Apache 2.0, ungated", "GGUF, FP8 and NVFP4 builds from StepFun", "Four released runtimes carry it, with Step parsers in vLLM and SGLang", "A 256K context with images in", "Hosted by StepFun and two providers" ], "weaknesses": [ "About 131 GB to run at 4 bits with images, over a 128 GB machine's share", "Every serve command on the card runs the repository's code", "No reply from StepFun on the repository's discussions", "Not changed since 3 June 2026", "No limitations, safety results or training data on the card" ], "agentNotes": [ "On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server", "On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`", "The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code", "For llama.cpp load the mmproj file from StepFun's GGUF repository for image input", "StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`", "The same model is on our StepFun API listing, with prices and terms" ], "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": 59.8 } ], "editorialScores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "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\"\nsglang serve --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code", "http": "# SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1\n# (https://api.stepfun.com/v1 for keys issued in China)" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/step-3-7-flash" }, "sameModel": [ "stepfun-api" ], "weights": { "trainedBy": "StepFun", "params": "198B", "active": "11B", "context": 262144, "goodFor": "Agent work that mixes images, search and code, on a GPU server or through StepFun's API. It's a few GB too big for a 128 GB machine at 4 bits.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Step-3.7-Flash-GGUF IQ4_XS", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 108.96, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "104.99 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-GGUF Q4_K_S", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q4_k_s", "sizeGB": 115.47, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "111.50 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-NVFP4", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 129.25, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's 4-bit build for NVIDIA GPUs" }, { "name": "Step-3.7-Flash-FP8", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 212.53, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's FP8 build" }, { "name": "Step-3.7-Flash-GGUF Q8_0", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 213.39, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "209.42 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash (BF16 safetensors)", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "who": "publisher", "precision": "bf16", "sizeGB": 402.73, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "26 files" } ], "run": "llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:IQ4_XS --jinja", "runBuild": "Step-3.7-Flash-GGUF IQ4_XS", "checked": "2026-11-10", "minMemoryGB": 192 }, "family": { "lab": "StepFun", "family": "Step 3", "url": "https://www.anchorterminal.com/open-weights/#stepfun", "others": [ { "name": "Step-3.5-Flash", "url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash" }, { "name": "Step3-VL-10B", "url": "https://huggingface.co/stepfun-ai/Step3-VL-10B" } ] }, "area": "models", "provenance": { "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED", "domain": "stepfun.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/stepfun-ai/Step-3.7-Flash/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "StepFun's legal entity, domain and security.txt are carried from our StepFun API listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing.", "The changelog is the commit history, 23 commits from 23 May to 3 June 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.json" }, "edges": [ { "by": 25, "category": "interface", "leads": "step-3-7-flash", "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, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 60, "b": 85 } }, { "by": 22, "category": "upkeep", "leads": "kronos", "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 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39." }, "scores": { "a": 61, "b": 39 } }, { "by": 15, "category": "reach", "leads": "step-3-7-flash", "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 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70." }, "scores": { "a": 55, "b": 70 } }, { "by": 5, "category": "risk", "leads": "kronos", "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 and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45." }, "scores": { "a": 50, "b": 45 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "b": "StepFun", "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 (metadata and card; 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": "2026-06-03", "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": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and documentation and upkeep.", "question": "Which is better for AI agents, Kronos or Step 3.7 Flash?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 45", "Documentation and upkeep, 61 against 39" ], "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, 70 against 55", "Agent interface, 85 against 60" ], "also": null, "goodFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "slug": "step-3-7-flash", "watchFor": "About 131 GB to run at 4 bits with images, over a 128 GB machine's share" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { 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{ "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash.json", "title": "LFM2.5-2.6B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash.json", "title": "LFM3-1.7B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash.json", "title": "LFM3-Audio-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.json", "title": "LFM3-Omni-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json", "title": "LFM3-VL-2B vs Step 3.7 Flash", "url": 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"Qwen3.5-397B-A17B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.json", "title": "Qwen3.5-4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json", "title": "Qwen3.5-9B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.json", "title": "Qwen3.6-27B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash.json", "title": "Qwen3.6-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-2B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-32B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-8B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.json", "title": "Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash.json", "title": "Recursive Flint vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.json", "title": "Ripple-65B Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json", "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.json", "title": "SmolLM3-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json", "title": "Spark-X2.6-4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.json", "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json", "title": "Step 3.7 Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "step-3-7-flash", "key": "reach", "kronos": 55, "name": "Reach", "step-3-7-flash": 70, "weight": 25 }, { "by": 5, "edge": "kronos", "key": "risk", "kronos": 50, "name": "Risk", "step-3-7-flash": 45, "weight": 20 }, { "by": 25, "edge": "step-3-7-flash", "key": "interface", "kronos": 60, "name": "Agent interface", "step-3-7-flash": 85, "weight": 10 }, { "by": 22, "edge": "kronos", "key": "upkeep", "kronos": 61, "name": "Documentation and upkeep", "step-3-7-flash": 39, "weight": 10 } ], "summary": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and documentation and upkeep. 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.", "step-3-7-flash": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash", "json": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.min.md" }, "markdown": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and documentation and upkeep. 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- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.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\nAhead on:\n- Risk, 50 against 45\n- Documentation and upkeep, 61 against 39\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### Step 3.7 Flash (C)\n\nGood for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision\n\nAhead on:\n- Reach, 70 against 55\n- Agent interface, 85 against 60\n\nWatch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share\n\n\n## Why the edge\n\n### Agent interface: Step 3.7 Flash +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- Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Documentation and upkeep: Kronos +22\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- Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39.\n\n### Reach: Step 3.7 Flash +15\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- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.\n\n### Risk: Kronos +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- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n## Score by category\n\n| Category | Weight | Kronos | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 70 | Step 3.7 Flash +15 |\n| Risk | 20% (20 this run) | 50 | 45 | Kronos +5 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Step 3.7 Flash +25 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 39 | Kronos +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Kronos | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | StepFun |\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 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-13 | 2026-06-03 |\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**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.\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### Step 3.7 Flash\n\n1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server\n2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`\n3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code\n4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input\n5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`\n6. The same model is on our StepFun API listing, with prices and terms\n\n## Questions\n\n### Which is better for AI agents, Kronos or Step 3.7 Flash?\n\nStep 3.7 Flash scores 59.8 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"step-3-7-flash\"}`. 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