MIC SmolLM-integration (files_sha256 ec9749992669)

README.md

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+---
+tags:
+  - mic
+  - test-artifact
+base_model:
+  - HuggingFaceTB/SmolLM-135M
+  - HuggingFaceTB/SmolLM-360M
+  - HuggingFaceTB/SmolLM-1.7B
+---
+
+# MIC: SmolLM text generation
+
+Model integration contract for one usage path, batched greedy generation, across the SmolLM checkpoints that share it. The proposed owner is Hugging Face; frameworks pin this repository by revision. The model repositories are unchanged. This repository holds the contract code and the manifest; each framework keeps its own expected results. It is a test artifact, not a model: exclude it from model search.
+
+For agent reuse, the real model must link to this contract or expose a lookup, together with a last-passing environment and the reviewed baseline's location. A contract hidden from model search still needs to be discoverable from its covered checkpoints. A fixture pass and a real-checkpoint pass have different scopes; either establishes agreement on fixed inputs and a recorded runtime, not correctness on arbitrary prompts.
+
+| File | What it is |
+| --- | --- |
+| [`integration.json`](integration.json) | Manifest: checkpoints, which fixture covers each, frameworks, entry points, variants, unsupported targets, comparison rules |
+| [`integrations/transformers/usage.md`](integrations/transformers/usage.md) | Transformers contract, written as doc-builder runnable markdown: the same blocks are documentation and test |
+| [`integrations/transformers.js/integration.js`](integrations/transformers.js/integration.js) | Transformers.js contract on the `onnx-community` conversions; inference only |
+
+| Checkpoint | Slim fixture (every framework PR) | Transformers.js artifact |
+| --- | --- | --- |
+| `HuggingFaceTB/SmolLM-135M` | [`hf-internal-testing/proto-SmolLM-slim`](https://huggingface.co/hf-internal-testing/proto-SmolLM-slim) | `onnx-community/SmolLM-135M-ONNX` |
+| `HuggingFaceTB/SmolLM-360M` | [`hf-internal-testing/proto-SmolLM-slim`](https://huggingface.co/hf-internal-testing/proto-SmolLM-slim) | `onnx-community/SmolLM-360M-ONNX` |
+| `HuggingFaceTB/SmolLM-1.7B` | [`hf-internal-testing/proto-SmolLM-1.7B-slim`](https://huggingface.co/hf-internal-testing/proto-SmolLM-1.7B-slim) | unsupported: no `onnx-community` conversion |
+
+135M and 360M share one fixture because they have the same fixture key: byte-identical processor files and the same config wherever shrinking keeps it. 1.7B ships a different `tokenizer.json` and `tokenizer_config.json` and uses full multi-head attention, so it gets its own fixture.

integration.json

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+{
+  "mic": "0.2",
+  "name": "SmolLM text generation",
+  "usage": "AutoTokenizer + AutoModelForCausalLM.generate, batched, left padding",
+  "architecture": "LlamaForCausalLM",
+  "checkpoints": {
+    "HuggingFaceTB/SmolLM-135M": {
+      "fixture": "hf-internal-testing/proto-SmolLM-slim"
+    },
+    "HuggingFaceTB/SmolLM-360M": {
+      "fixture": "hf-internal-testing/proto-SmolLM-slim"
+    },
+    "HuggingFaceTB/SmolLM-1.7B": {
+      "fixture": "hf-internal-testing/proto-SmolLM-1.7B-slim"
+    }
+  },
+  "fixtures": {
+    "hf-internal-testing/proto-SmolLM-slim": {
+      "built_from": "HuggingFaceTB/SmolLM-135M",
+      "covers": [
+        "HuggingFaceTB/SmolLM-135M",
+        "HuggingFaceTB/SmolLM-360M"
+      ]
+    },
+    "hf-internal-testing/proto-SmolLM-1.7B-slim": {
+      "built_from": "HuggingFaceTB/SmolLM-1.7B",
+      "covers": [
+        "HuggingFaceTB/SmolLM-1.7B"
+      ],
+      "why_separate": "different tokenizer.json and tokenizer_config.json; full multi-head attention instead of grouped key/value heads"
+    }
+  },
+  "integrations": {
+    "transformers": {
+      "entry_point": "integrations/transformers/usage.md",
+      "format": "runnable-markdown",
+      "requires": [
+        "transformers>=5",
+        "torch",
+        "accelerate"
+      ],
+      "targets": [
+        "fixtures",
+        "checkpoints"
+      ],
+      "variants": {
+        "default": {},
+        "eager": {
+          "attn_implementation": "eager"
+        },
+        "bfloat16": {
+          "dtype": "bfloat16"
+        }
+      },
+      "training": true,
+      "resources": {
+        "fixture": {
+          "memory_gb": 1,
+          "download_mb": 16
+        },
+        "HuggingFaceTB/SmolLM-135M": {
+          "memory_gb": 2,
+          "download_mb": 513
+        },
+        "HuggingFaceTB/SmolLM-360M": {
+          "memory_gb": 3,
+          "download_mb": 1300
+        },
+        "HuggingFaceTB/SmolLM-1.7B": {
+          "memory_gb": 12,
+          "disk_gb": 8,
+          "download_mb": 6400,
+          "measured": "peak RSS 10.6 GB (default) and 11.3 GB (bfloat16: the variant reload holds two copies), one process, Apple M5, 2026-10-07"
+        }
+      }
+    },
+    "transformers.js": {
+      "entry_point": "integrations/transformers.js/integration.js",
+      "format": "script",
+      "requires": [
+        "@huggingface/transformers@4.3.0"
+      ],
+      "targets": [
+        "checkpoints"
+      ],
+      "artifacts": {
+        "HuggingFaceTB/SmolLM-135M": {
+          "repo": "onnx-community/SmolLM-135M-ONNX",
+          "file": "onnx/model.onnx"
+        },
+        "HuggingFaceTB/SmolLM-360M": {
+          "repo": "onnx-community/SmolLM-360M-ONNX",
+          "file": "onnx/model.onnx"
+        }
+      },
+      "unsupported": {
+        "fixtures": "no ONNX conversion of the fixtures yet",
+        "HuggingFaceTB/SmolLM-1.7B": "no onnx-community conversion; the creator's float32 ONNX is 6.8 GB"
+      },
+      "variants": {
+        "default": {
+          "dtype": "fp32"
+        },
+        "webgpu": {
+          "dtype": "fp32",
+          "device": "webgpu"
+        },
+        "webgpu-fp16": {
+          "dtype": "fp16",
+          "device": "webgpu"
+        },
+        "webgpu-q4f16": {
+          "dtype": "q4f16",
+          "device": "webgpu"
+        }
+      },
+      "training": false
+    }
+  },
+  "outputs": {
+    "runtime": {
+      "compare": "exact"
+    },
+    "generated_tokens": {
+      "compare": "exact"
+    },
+    "generated_text": {
+      "compare": "exact"
+    },
+    "next_token_logits": {
+      "compare": "allclose",
+      "atol": 0.0001,
+      "rtol": 0.0001
+    },
+    "training.loss": {
+      "compare": "allclose",
+      "atol": 0.0001,
+      "rtol": 0.0001
+    },
+    "training.gradients": {
+      "compare": "all_true"
+    },
+    "training.updated": {
+      "compare": "true"
+    }
+  }
+}

integrations/transformers.js/integration.js

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+// Transformers.js contract of the SmolLM MIC: batched greedy generation.
+//
+// Runs on HF-owned ONNX conversions (onnx-community/SmolLM-*-ONNX), not on the
+// creator's PyTorch checkpoints. The runner sets MIC_MODEL, MIC_MODEL_REVISION,
+// and MIC_MODEL_KWARGS (JSON). The last stdout line is the result record.
+// Inference only: Transformers.js does not train, so there is no training probe.
+// Timings are recorded for performance tracking but not compared with baselines.
+
+import { AutoModelForCausalLM, AutoTokenizer } from "@huggingface/transformers";
+
+const modelId = process.env.MIC_MODEL ?? "onnx-community/SmolLM-135M-ONNX";
+const revision = process.env.MIC_MODEL_REVISION || "main";
+const modelKwargs = JSON.parse(process.env.MIC_MODEL_KWARGS || '{"dtype": "fp32"}');
+
+const prompts = [
+  "The capital of France is",
+  "The capital of Germany is",
+  "The capital of Italy is",
+  "The capital of the United Kingdom is",
+];
+const answers = [" Paris", " Berlin", " Rome", " London"];
+
+const tokenizer = await AutoTokenizer.from_pretrained(modelId, { revision });
+tokenizer.pad_token = tokenizer.eos_token;
+tokenizer.pad_token_id = tokenizer.eos_token_id;
+tokenizer.padding_side = "left";
+let start = performance.now();
+const model = await AutoModelForCausalLM.from_pretrained(modelId, { revision, ...modelKwargs });
+const timing = { load_ms: performance.now() - start };
+
+const inputs = tokenizer(prompts, { padding: true });
+const generate = () =>
+  model.generate({ ...inputs, max_new_tokens: 4, do_sample: false, pad_token_id: tokenizer.eos_token_id });
+start = performance.now();
+await generate(); // first call compiles kernels and allocates buffers
+timing.first_generate_ms = performance.now() - start;
+start = performance.now();
+const generated = await generate();
+timing.generate_ms = performance.now() - start;
+const promptLength = inputs.input_ids.dims[1];
+const generatedTokens = generated.tolist().map((row) => row.slice(promptLength).map(Number));
+console.error(tokenizer.batch_decode(generatedTokens, { skip_special_tokens: true }));
+
+start = performance.now();
+const { logits } = await model(inputs);
+timing.forward_ms = performance.now() - start;
+const answerIds = answers.map((a) => tokenizer.encode(a, { add_special_tokens: false })[0]);
+const [batch, length, vocab] = logits.dims;
+const nextTokenLogits = [];
+for (let b = 0; b < batch; b++) {
+  const offset = (b * length + length - 1) * vocab;
+  nextTokenLogits.push(answerIds.map((id) => Number(logits.data[offset + id])));
+}
+
+console.log(
+  JSON.stringify({
+    runtime: { device: modelKwargs.device ?? "cpu", dtype: modelKwargs.dtype ?? "fp32" },
+    generated_tokens: generatedTokens,
+    generated_text: tokenizer.batch_decode(generatedTokens, { skip_special_tokens: true }),
+    next_token_logits: nextTokenLogits,
+    timing,
+  }),
+);

integrations/transformers.js/package-lock.json

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integrations/transformers.js/package.json

@@ -0,0 +1,8 @@
+{
+  "name": "smollm-mic-transformers-js",
+  "private": true,
+  "type": "module",
+  "dependencies": {
+    "@huggingface/transformers": "4.3.0"
+  }
+}

integrations/transformers/usage.md

@@ -0,0 +1,79 @@
+# SmolLM: batched text generation with Transformers
+
+This page is the Transformers contract of the SmolLM MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `HuggingFaceTB/SmolLM-135M`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record. They do not appear in rendered docs.
+
+This example's results apply to the target and runtime recorded with its reviewed baseline. Fixture outputs differ from real-checkpoint outputs, and agreement on these prompts does not establish correctness on arbitrary prompts. To reproduce a tested run, use its pinned framework build and environment rather than infer compatibility from the model's serialization metadata. The enclosing [example](../../../../README.md) records the current local setup and remaining publication work.
+
+## Generate
+
+```py runnable:generate
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json  # doc-builder: hide
+import os  # doc-builder: hide
+
+model_id = "HuggingFaceTB/SmolLM-135M"
+model_id = os.environ.get("MIC_MODEL", model_id)  # doc-builder: hide
+variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}"))  # doc-builder: hide
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "left"
+model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+prompts = [
+    "The capital of France is",
+    "The capital of Germany is",
+    "The capital of Italy is",
+    "The capital of the United Kingdom is",
+]
+if variant:  # doc-builder: hide
+    model = AutoModelForCausalLM.from_pretrained(model_id, **variant).to(device)
+inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
+outputs = model.generate(**inputs, max_new_tokens=4, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+new_tokens = outputs[:, inputs["input_ids"].shape[1] :]
+print(tokenizer.batch_decode(new_tokens, skip_special_tokens=True))
+
+with torch.inference_mode():  # doc-builder: hide
+    logits = model(**inputs).logits
+    answer_ids = [tokenizer.encode(a, add_special_tokens=False)[0] for a in (" Paris", " Berlin", " Rome", " London")]
+    next_token_logits = logits[:, -1, answer_ids].float()
+```
+
+## One training step
+
+```py runnable:generate:2
+answers = [" Paris", " Berlin", " Rome", " London"]
+tokenizer.padding_side = "right"  # pad after the text for training, so every label has real context
+batch = tokenizer([p + a for p, a in zip(prompts, answers)], return_tensors="pt", padding=True).to(device)
+labels = batch["input_ids"].masked_fill(batch["attention_mask"] == 0, -100)
+
+model.train()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+loss = model(**batch, labels=labels).loss
+loss.backward()
+before = {name: p.detach().clone() for name, p in model.named_parameters()}  # doc-builder: hide
+optimizer.step()
+print(f"loss: {loss.item():.4f}")
+
+if os.environ.get("MIC_RUNNER"):  # doc-builder: hide
+    named = dict(model.named_parameters())
+    checked = ("model.layers.0.self_attn.q_proj.weight", "model.layers.0.mlp.down_proj.weight")
+    result = {
+        "runtime": {
+            "device": model.device.type,
+            "dtype": str(model.dtype).removeprefix("torch."),
+            "attn_implementation": model.config._attn_implementation,
+        },
+        "generated_tokens": new_tokens.tolist(),
+        "generated_text": tokenizer.batch_decode(new_tokens, skip_special_tokens=True),
+        "next_token_logits": next_token_logits.tolist(),
+        "training": {
+            "loss": loss.item(),
+            "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked},
+            "updated": any(not torch.equal(p, before[n]) for n, p in named.items()),
+        },
+    }
+    print(json.dumps(result))
+```