GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
nkthebass
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
nkthebass
Model Tree
nkthebass/tinybrainbot-360m-a75m-v5.1-instruct
Input Modalities
Output Modalities
Supported Functionality
| v5.1-Chat (this) | v5.1-Instruct | |
|---|---|---|
| Word problems asked in the middle of a chat (12) | 12 | 6 |
| Knows its name and maker (12 questions, fresh and mid-chat) | 11 | 8 |
| Declines violent / self-harm requests | 100% | 100% |
| Declines harmful requests (all) | 95% | 97% |
| Wrongly refuses benign requests | 3% | 3% |
| Supportive reply to distress | 83% | 85% |
| IFEval prompt / instruction-level strict | 27.2 / 40.3 | 28.3 / 40.8 |
| Open SLM Leaderboard Intelligence Index | 27.91 | 27.94 |
| (raw acc) | 25.48 | 25.80 |
The Chat version gives up a little formal instruction following (IFEval) for casual conversation; knowledge benchmarks stay the same. Chat arithmetic (2- to 4-digit, 25 problems per kind) is 100% on every kind.
| Benchmark (0-shot, full set) | acc_norm | (raw acc) |
|---|---|---|
| HellaSwag | 38.80 | 33.15 |
| ARC-Easy | 58.92 | 62.25 |
| ARC-Challenge | 36.18 | 33.79 |
| PIQA | 66.21 | 65.23 |
| ArithMark-3 | 49.20 | 49.20 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "nkthebass/tinybrainbot-360m-a75m-v5.1-chat"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)msgs = [{"role": "user", "content": "yo bro whats good"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=200) # defaults from generation_config.jsonprint(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))GGUF (llama.cpp, LM Studio, Jan, Ollama): Q8_0 (409 MB), Q4_K_M (239 MB) or F16 (768 MB). The chat template is embedded, chats tokenize exactly like transformers, and greedy F16 output matches transformers token for token.
text
llama-cli -m tinybrainbot-360m-a75m-v5.1-chat-Q8_0.gguf -cnv --temp 0.4 --top-p 0.9 --repeat-penalty 1.15 --logit-bias 241-inf --logit-bias 255-infRecommended sampling: temperature 0.4, top-p 0.9, repetition penalty 1.15. The --logit-bias flags block the byte
tokens that start every emoji (the tokenizer has no emoji pieces). If a reply starts with a <think> block, it's plain
text: strip it with re.sub(r"<think>.*?</think>", "", reply, flags=re.S).
Starts from the v5.1-Instruct SFT and adds one short SFT stage (400 steps, 52M tokens):
| Part | Share | What |
|---|---|---|
| chill-chat | 15% | 10,081 casual chats written for this model by small local models: granite-4.0-1b wrote everyday chat, venting and banter (and played the user in every chat), LFM3-1.7B wrote chats about the bot itself, its creator, hobbies, opinions and remembering what you said, LFM2.5-1.2B wrote practical help. The teachers were picked in a bake-off on real chat logs. Filtered: no emoji or markdown, assistant-speak cut, cut-off replies, human-life claims and other models' names dropped, replies capped at 55 words |
| EmpatheticDialogues | 6% | human-written conversations where one side listens and reacts, with any reply that claims a human life removed |
| Mid-chat skills | 5.7% | word problems, "who are you" questions and safety cases placed after a few casual turns, so it doesn't lose them in a conversation |
| Replay | 73.3% | the v5.1-Instruct SFT mix (chat, math, reasoning, instruction following) so nothing is forgotten |
No benchmark training or test data was used. Part of the chat data was written by granite-4.0-1b (Apache-2.0) and the LFM models (LFM Open License), and EmpatheticDialogues is CC BY-NC 4.0: check those terms before any commercial use.
<think> block that sizes up the question ("a simple question about a food
debate...") before answering; the answer after it is normal.from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "nkthebass/tinybrainbot-360m-a75m-v5.1-chat"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)msgs = [{"role": "user", "content": "yo bro whats good"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=200) # defaults from generation_config.jsonprint(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))