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Model: nkthebass/TinyBrainBot-303m-instruct
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- instruct
+- chat
+- conversational
+- small-language-model
+---
+
+# TinyBrainBot 303M — Instruct
+
+A **303M-parameter chat assistant** built from scratch on a home server (2× NVIDIA Tesla P100): pretrained → fact-distilled → supervised fine-tuned. It's a tiny, honest little assistant — it answers common questions, follows simple instructions, holds a short conversation, and (often) **admits when it doesn't know** instead of bluffing.
+
+Base (pretrained-only) version: **TinyBrainBot 303M Base**.
+
+## Model details
+
+| | |
+|---|---|
+| Parameters | ~303M |
+| Architecture | LLaMA-style (`LlamaForCausalLM`) — RoPE, RMSNorm, SwiGLU, GQA |
+| Layers / hidden / heads | 24 / 1024 / 16 (4 KV heads) |
+| FFN / vocab / context | 2816 / 32,000 / 1024 |
+| Tied embeddings | Yes |
+| Special tokens | `<\|user\|>` `<\|assistant\|>` `<\|system\|>` `<\|end\|>` |
+| EOS token | `<\|end\|>` |
+
+## ⚠️ Chat template — use SPACES, not newlines
+
+This is the single most important detail. The tokenizer normalizes newlines to spaces, so the model was trained with **spaces** between turns. The bundled `chat_template` already does this — **use `apply_chat_template`** and it just works:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tok = AutoTokenizer.from_pretrained("your-username/tinybrainbot-303m-instruct")
+model = AutoModelForCausalLM.from_pretrained("your-username/tinybrainbot-303m-instruct", torch_dtype=torch.float16).eval()
+
+msgs = [{"role": "user", "content": "What is the capital of France?"}]
+prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+ids = tok(prompt, return_tensors="pt", add_special_tokens=False).input_ids
+out = model.generate(ids, max_new_tokens=64, do_sample=True,
+                     temperature=0.5, top_p=0.9, repetition_penalty=1.2, eos_token_id=7)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+# -> Paris.
+```
+
+The rendered format is (note the **spaces**, no `\n`):
+```
+<|user|> {message} <|end|> <|assistant|>
+```
+If you build prompts by hand or configure a UI (llama.cpp / Ollama / LM Studio / Jan), **make sure the separators are spaces and the stop token is `<|end|>`** — a template with literal `\n` will feed the model out-of-distribution tokens and it will ramble without stopping.
+
+## Recommended sampling
+
+Small models need a **lower temperature** than large ones (temp 1.0 makes this model incoherent).
+
+| Use case | temp | top-p | rep penalty |
+|---|---|---|---|
+| General chat (default) | 0.7 | 0.9 | 1.2 |
+| More creative | 0.8 | 0.9 | 1.2 |
+| Factual / reliable | 0.4–0.6 | 0.9 | 1.15 |
+
+## Training
+
+- **Base:** pretrained from scratch on FineWeb-Edu, Wikipedia, TinyStories, OpenWebText2, Orca-Math.
+- **Fact distillation:** synthetic Q&A + short-fact datasets generated by a stronger teacher (Ling 2.6 Flash), mixed into continued pretraining to densify factual recall.
+- **SFT:** ~33k instruction→response pairs plus short 2-turn conversations, distilled from the same teacher, spanning: general instructions, concise Q&A, explain/summarize/rewrite/list/compare/how-to, short creative writing, honest **"I don't know"** examples, and polite refusals. Loss masked to the assistant turns only.
+
+## Evaluation
+
+Standard multiple-choice benchmarks (log-likelihood scoring, n=200 subsample — **absolute numbers run a bit low vs. the official lm-eval-harness**; use them for relative comparison):
+
+| Benchmark | TinyBrainBot 303M | random |
+|---|---|---|
+| ARC-Easy (acc_norm) | ~44% | 25 |
+| OpenBookQA (acc_norm) | ~31% | 25 |
+| HellaSwag (acc_norm) | ~29% | 25 |
+| WinoGrande (acc) | ~46% | 50 |
+| ARC-Challenge (acc_norm) | ~24% | 25 |
+| MMLU (acc) | ~23% | 25 |
+
+Roughly **GPT-2-small tier** on general reasoning, with a relative edge on knowledge QA (ARC / OpenBookQA) from the fact distillation. Near-random on MMLU, as expected at this scale.
+
+## Sampling
+
+ (best of 5 samples, temperature tuned per prompt):
+
+Q: What is the largest planet in the solar system?
+Jupiter.
+
+Q: Explain what photosynthesis is in one or two simple sentences.
+Photosynthesis is how plants use sunlight, water, and air to make food (glucose) and release oxygen. It occurs in many types like green plants, algae, and trees.
+
+Q: Write a short poem about the moon.
+Silver moon, slow and gold, hums low over the sleeping sky. It dreams in silver rivers, guarding dreams in morning light.
+
+Q: List three healthy breakfast ideas.
+Greek yogurt with berries, whole wheat bread, cream cheese.
+
+Q: Give me one tip for staying focused while studying.
+Use 25-minute focus blocks with 5-minute breaks, silence notifications, and a clear workspace to keep your mind fresh.
+
+## Limitations
+
+- **Fragile facts.** Sensitive to phrasing and capitalization; standard, well-formed questions work best. Confidently wrong on the long tail — pair with **retrieval (RAG)** for anything important.
+- **Weak reasoning/math** — it's 303M.
+- The "I don't know" and refusal behaviors are **helpful but not 100% reliable** (they were a small slice of SFT).
+- English only.
+
+## License
+
+Apache-2.0 *(change if you prefer)*.