{ "data": { "a": { "slug": "agents-a1-4b", "name": "Agents-A1-4B", "vendor": "InternScience (Shanghai AI Laboratory)", "vendorUrl": "https://internscience.github.io/Agents-A1/", "kind": "weights", "category": "open-weights", "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/agents-a1-4b", "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json", "repo": "https://huggingface.co/InternScience/Agents-A1-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "InternScience/Agents-A1-4B" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-FP8" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q8_0-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-F16-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (checked 2026-10-21).", "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 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-07-14", "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": 1197, "categoryRank": 27, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "strengths": [ "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model", "FP8 and three GGUF precisions from the publisher", "Apache 2.0 through the chain, with no gate", "Trained for tool calling, several calls a turn, and 262,144 tokens of context", "An evaluation framework for its agent benchmarks is published on GitHub" ], "weaknesses": [ "The base isn't declared, and the technical report doesn't cover the 4B", "No safety results, agent guidance or security contact", "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14", "Tool calling and structured output aren't documented beyond a parser name", "InternScience answered one of three discussions, and the history was squashed" ], "agentNotes": [ "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector", "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work", "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14", "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/agents-a1-4b" }, "weights": { "trainedBy": "InternScience (Shanghai AI Laboratory)", "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B", "params": "4.5B", "context": 262144, "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Agents-A1-4B-Q8_0", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 5.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" }, { "name": "Agents-A1-4B-Q4_K_M", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.7 GB of weights and the 0.7 GB vision projector" }, { "name": "Agents-A1-4B", "url": "https://huggingface.co/InternScience/Agents-A1-4B", "who": "publisher", "precision": "bf16", "sizeGB": 9.1, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Agents-A1-4B-FP8", "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 7.8, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Agents-A1-4B-F16", "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF", "who": "publisher", "precision": "f16", "sizeGB": 9.1, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" } ], "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Agents-A1-4B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "InternScience (Shanghai AI Laboratory)", "family": "Agents-A1", "url": "https://www.anchorterminal.com/open-weights/#internscience", "others": [ { "name": "Agents-A1", "url": "https://huggingface.co/InternScience/Agents-A1", "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template", "domain": "internscience.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.", "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.", "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json" }, "answer": "Agents-A1-4B scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on documentation and upkeep.", "b": { "slug": "ling-3-0-tiny", "name": "Ling-3.0-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "Ling-3.0-tiny is inclusionAI's open-weight hybrid reasoning mixture of experts with 7.9B parameters and 1.3B active, mixing Kimi Delta Attention and MLA layers 3 to 1, with switchable thinking and a 131,072-token context, in BF16, FP8, INT4 and GGUF under MIT.", "url": "https://www.anchorterminal.com/tools/ling-3-0-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-fp8" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-int4" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-GGUF" } ], "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 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repositories hold no LICENSE file). Running it yourself costs the hardware, 15.8 GB for the BF16 files, 8.4 GB for the FP8 build, 5.8 GB for INT4 and 4.8 GB for the Q4_K_M GGUF (checked 2026-10-21).", "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 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "hybrid-attention", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "int4", "gguf", "self-hosted" ], "lastRelease": "2026-08-31", "graded": true, "anchor": { "graded": true, "score": 55, "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": 1197, "categoryRank": 65, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.", "bestFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT", "strengths": [ "About 6 GB of memory for inclusionAI's own Q4_K_M GGUF, with FP8, INT4 and three more GGUF precisions", "Runs in released Transformers, vLLM, SGLang and llama.cpp", "MIT with no gate, no account and no use policy, with checksummed files in inclusionAI's own organisation", "Thinking switchable per request, several tool calls a turn and 131,072 tokens of native context", "A training-content summary with sources, size and an October 2025 cut-off, and a guardrail probe made for this model" ], "weaknesses": [ "No hosted provider today, and Ollama support is only an unmerged pull request", "The card's SGLang recipe asks for built-in MTP the files don't have, and the safetensors index misstates the total size", "The documented vLLM route needs --trust-remote-code", "No safety results for the model, no structured output and no security contact", "Card and SGLang disagree on which tool and reasoning parsers to use, and most discussions since August have no reply" ], "agentNotes": [ "Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3", "With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code", "With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks", "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20", "The template keeps earlier reasoning in the prompt, which grows long agent sessions", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented", "For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55 } ], "editorialScores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "provenanceScore": 57, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.32.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align \\\n --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ling-3.0-tiny\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}, \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 20}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ling-3-0-tiny" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "params": "7.9B (1.3B active)", "context": 131072, "goodFor": "Small, fast hybrid reasoning model for local agents, tool calling and coding on a laptop, with thinking switchable per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ling-3.0-tiny-int4", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4", "who": "publisher", "precision": "int4", "sizeGB": 5.8, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors, 4-bit weights in groups of 32, with attention and dense layers kept at higher precision" }, { "name": "Ling-3.0-tiny-Q6_K", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 6.5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q5_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 5.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q4_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 4.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-fp8", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 8.4, "memoryGB": 12, "format": "safetensors" }, { "name": "Ling-3.0-tiny-Q8_0", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 8.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 15.8, "memoryGB": 24, "format": "safetensors" } ], "run": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Ling-3.0-tiny-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ling 3", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ling-3.0-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-Fin", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-VL", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL", "params": "124B total, 5.5B active" }, { "name": "Ling-3.0-flash-dspark", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark", "params": "1.36B (draft)" }, { "name": "Ling-3.0-flash-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe", "params": "5.18M (probe)" }, { "name": "Ling-3.0-tiny-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe", "params": "3.22M (probe)" }, { "name": "Ling-3.1-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash", "params": "560B total, 25B active" } ], "olderCount": 54, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. 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"https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2.json", "title": "Ling-3.0-tiny vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b.json", "title": "Ling-3.0-tiny vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b.json", "title": "Ling-3.0-tiny vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "agents-a1-4b": 90, "by": 0, "edge": "", "key": "reach", "ling-3-0-tiny": 90, "name": "Reach", "weight": 25 }, { "agents-a1-4b": 55, "by": 5, "edge": "agents-a1-4b", "key": "risk", "ling-3-0-tiny": 50, "name": "Risk", "weight": 20 }, { "agents-a1-4b": 70, "by": 10, "edge": "agents-a1-4b", "key": "interface", "ling-3-0-tiny": 60, "name": "Agent interface", "weight": 10 }, { "agents-a1-4b": 43, "by": 13, "edge": "ling-3-0-tiny", "key": "upkeep", "ling-3-0-tiny": 56, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Agents-A1-4B scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "ling-3-0-tiny": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny", "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.min.md" }, "markdown": "Agents-A1-4B scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\n- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\n\n### Ling-3.0-tiny (C)\n\nGood for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT\n\nAhead on:\n- Documentation and upkeep, 56 against 43\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | Ling-3.0-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 55 | 50 | Agents-A1-4B +5 |\n| Agent interface | 10% (10 this run) | 70 | 60 | Agents-A1-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 56 | Ling-3.0-tiny +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **55 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Ling-3.0-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | inclusionAI (Ant Group) |\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 | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-08-31 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\n\n**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.\n\n## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Ling-3.0-tiny\n\n1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3\n2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code\n3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20\n6. The template keeps earlier reasoning in the prompt, which grows long agent sessions\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches\n\n## Questions\n\n### Which is better for AI agents, Agents-A1-4B or Ling-3.0-tiny?\n\nAgents-A1-4B scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"ling-3-0-tiny\"}`. 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