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README.md

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+---
+license: mit
+language: en
+tags:
+- function-calling
+- tiny-model
+- edge-ai
+- tool-use
+pipeline_tag: text-generation
+---
+
+# Tiny Function-Calling LM (~0.47M params)
+
+A from-scratch decoder-only transformer with ~471,760 parameters, trained to route
+natural-language requests to a single tool (`web_search`) or abstain (`none`).
+Built as a demonstration of function-calling on an extremely small budget.
+
+## Architecture
+- 4 transformer layers, d_model=80, 4 attention heads (head dim 20)
+- RoPE positional encoding, RMSNorm, GELU feed-forward (4x width), tied input/output embeddings
+- BPE tokenizer with a 2,048-token vocabulary trained on the task's own synthetic data
+- Context length: 80 tokens
+
+## Output format
+```
+web_search|query=<search terms>|recency=<day|week|any>
+none
+```
+
+## Loading
+This is **not** a registered `transformers` architecture — it uses a small custom
+model class. Load it with the reference implementation (`model.py`) from the
+companion GitHub repo:
+
+**Code:** https://github.com/YOUR_USERNAME/tiny-fc-lm
+
+```python
+import torch
+from safetensors.torch import load_file
+from tokenizers import Tokenizer
+from model import TinyLM  # from the GitHub repo
+
+state = load_file("model.safetensors")
+tok = Tokenizer.from_file("tokenizer.json")
+model = TinyLM(vocab=2048, d=80, n_layers=4, n_heads=4, ffn_mult=4, max_len=80)
+model.load_state_dict(state)
+model.eval()
+```
+See the GitHub repo's `infer.py` for constrained decoding and tool dispatch.
+
+## Training data
+80,000 synthetic (request, tool-call) pairs generated from templated phrasings
+across ~10 intents (news, weather, price, stock, how-to, sports scores, definitions,
+generic web search, and weekly recaps), plus chit-chat examples mapped to `none`.
+
+## Evaluation
+| Split | Exact match |
+|---|---|
+| In-distribution (val) | ~1.00 |
+| Held-out phrasing (OOD) | ~0.88 |
+
+## Limitations
+- Single tool only (`web_search`); not a general-purpose assistant.
+- Learned via memorized entity↔pattern associations rather than true entity
+  copying, so genuinely novel named entities (names/places never seen in training)
+  are sometimes replaced with a memorized default instead of preserved.
+- English only; no multi-turn context.
+
+## License
+MIT. Provided as-is for research/educational use.

config.json

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+{
+  "architecture": "tinylm-fc",
+  "vocab_size": 2048,
+  "d_model": 80,
+  "n_layers": 4,
+  "n_heads": 4,
+  "ffn_mult": 4,
+  "max_position_embeddings": 80,
+  "positional_encoding": "rope",
+  "normalization": "rmsnorm",
+  "activation": "gelu",
+  "tied_embeddings": true,
+  "num_parameters": 471760,
+  "task": "single-tool function calling (web_search) + abstain (none)"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c6933d390e964c2d03472251d0d7be434be5095e5f40d57611cb6b5a0ce1989
+size 1889192