ambrosfitz
/

smollm2-graphcot

@@ -1,199 +1,167 @@
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 ---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-360M
+datasets:
+- ambrosfitz/Wikipedia_GraphCoT_Summarization
+language:
+- en
+tags:
+- text-generation
+- summarization
+- chain-of-thought
+- graph-reasoning
+- fine-tuned
+pipeline_tag: text-generation
 ---
+# SmolLM2-360M — GraphCoT Fine-tune
+Fine-tuned version of [SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on the [Wikipedia GraphCoT Summarization](https://huggingface.co/datasets/ambrosfitz/Wikipedia_GraphCoT_Summarization) dataset.
+The model learns to perform **semantic graph chain-of-thought (GraphCoT) summarization**: given a raw paragraph, it explicitly constructs a semantic knowledge graph before writing a summary grounded exclusively in that graph. No fact in the summary can appear unless it exists as an edge.
+---
+## What it does
+Given a paragraph wrapped in `<input>` tags, the model generates a structured `<reasoning>` block followed by a `<summary>`:
+1. **Content words** — extracts key nouns, verbs, and proper entities
+2. **Coreference** — resolves pronouns and aliases to a single primary subject
+3. **Relations** — builds `(subject, relation, object)` triples in snake_case
+4. **Graph** — organises triples into a rooted ASCII tree
+5. **Summary** — writes 1–3 sentences where every clause maps to a graph edge
+---
+## Example output
+**Input:**
+```
+Alan Turing was a British mathematician and computer scientist who played a
+crucial role in breaking German ciphers during World War II. He is widely
+regarded as the father of theoretical computer science and artificial intelligence.
+```
+**Model output:**
+```
+<reasoning>
+<content_words>
+  nouns: mathematician, computer, scientist, role, cipher, father, theory, intelligence
+  verbs: break, regard
+  proper_entities: Alan Turing, British, World War II, German
+</content_words>
+<coref>
+  (none)
+</coref>
+<relations>
+  (Alan Turing, is_a, British mathematician)
+  (Alan Turing, is_a, computer scientist)
+  (Alan Turing, broke, German ciphers)
+  (Alan Turing, broke_during, World War II)
+  (Alan Turing, is_the_father_of, theoretical computer science)
+  (Alan Turing, is_the_father_of, artificial intelligence)
+</relations>
+<graph>
+ROOT: Alan Turing
+├─ is_a -> British mathematician
+├─ is_a -> computer scientist
+├─ broke -> German ciphers
+│  └─ broke_during -> World War II
+├─ is_the_father_of -> theoretical computer science
+└─ is_the_father_of -> artificial intelligence
+</graph>
+</reasoning>
+<summary>
+Alan Turing was a British mathematician and computer scientist who broke German
+ciphers during World War II. He is regarded as the father of theoretical computer
+science and artificial intelligence.
+</summary>
+```
+---
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "ambrosfitz/smollm2-graphcot"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+paragraph = "Your paragraph here."
+prompt = f"<input>\n{paragraph}\n</input>\n\n<reasoning>\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=400,
+        do_sample=False,
+        repetition_penalty=1.1,
+    )
+print(tokenizer.decode(output_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+---
+## Training
+### Dataset
+[ambrosfitz/Wikipedia_GraphCoT_Summarization](https://huggingface.co/datasets/ambrosfitz/Wikipedia_GraphCoT_Summarization) — 6,856 Wikipedia paragraphs processed through a two-stage pipeline:
+- **Stage 1 (local):** spaCy scaffold — content word extraction, dependency triples, coreference clustering
+- **Stage 2 (LLM):** Gemini 2.5 Flash normalization — semantic edge labelling, tree assembly, grounded summary generation
+| Split | Records |
+|-------|---------|
+| Train | 6,172 |
+| Validation | 342 |
+| Test | 342 |
+### Loss masking
+Only `<reasoning>` and `<summary>` tokens contribute to the loss. The `<input>` paragraph is masked (`label = -100`) so the model learns to *generate* the graph and summary, not memorise the input.
+### Hyperparameters
+| Parameter | Value |
+|-----------|-------|
+| Base model | HuggingFaceTB/SmolLM2-360M |
+| Epochs | 3 |
+| Effective batch size | 16 (8 × 2 grad accum) |
+| Learning rate | 2e-5 |
+| LR schedule | Cosine with 100 warmup steps |
+| Max sequence length | 1024 tokens |
+| Precision | fp16 (AMP) |
+| Gradient checkpointing | Yes |
+| Hardware | NVIDIA T4 (Google Colab) |
+| Training time | ~2h 18m |
+### Training curves
+| Step | Train Loss | Eval Loss |
+|------|-----------|-----------|
+| 100  | 0.520 | 0.497 |
+| 300  | 0.369 | 0.367 |
+| 500  | 0.315 | 0.335 |
+| 700  | 0.310 | 0.320 |
+| 900  | 0.260 | 0.314 |
+| 1100 | 0.278 | 0.312 |
+| 1158 | 0.282 | 0.312 |
+Train and validation loss stayed within ~0.03 throughout — no overfitting.
+---
+## Limitations
+- Trained on Wikipedia-style encyclopaedic paragraphs; may produce lower-quality graphs on conversational or highly technical text
+- 360M parameters — graph structure may be incomplete or inconsistent on long or complex inputs
+- Max context 1024 tokens; paragraphs longer than ~700 words will be truncated