Text Generation
Transformers
Safetensors
English
llama
my-little-pony
mlp
fanfiction
small-language-model
from-scratch
lissa
roleplay
rp
character
edge-deployment
text-generation-inference
Instructions to use CastIronMind/LissaNano-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CastIronMind/LissaNano-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CastIronMind/LissaNano-Instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CastIronMind/LissaNano-Instruct") model = AutoModelForCausalLM.from_pretrained("CastIronMind/LissaNano-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CastIronMind/LissaNano-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CastIronMind/LissaNano-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CastIronMind/LissaNano-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CastIronMind/LissaNano-Instruct
- SGLang
How to use CastIronMind/LissaNano-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "CastIronMind/LissaNano-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CastIronMind/LissaNano-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "CastIronMind/LissaNano-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CastIronMind/LissaNano-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CastIronMind/LissaNano-Instruct with Docker Model Runner:
docker model run hf.co/CastIronMind/LissaNano-Instruct
Initial release: model, tokenizer, card
Browse files- README.md +173 -0
- config.json +32 -0
- generation_config.json +8 -0
- model.safetensors +3 -0
- special_tokens_map.json +5 -0
- tokenizer.json +0 -0
- tokenizer_config.json +52 -0
README.md
ADDED
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| 1 |
+
---
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| 2 |
+
language:
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| 3 |
+
- en
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| 4 |
+
- ru
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| 5 |
+
license: apache-2.0
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| 6 |
+
library_name: transformers
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| 7 |
+
pipeline_tag: text-generation
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| 8 |
+
pretty_name: LissaNano
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| 9 |
+
tags:
|
| 10 |
+
- text-generation
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| 11 |
+
- llama
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| 12 |
+
- my-little-pony
|
| 13 |
+
- mlp
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| 14 |
+
- roleplay
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| 15 |
+
- rp
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| 16 |
+
- character
|
| 17 |
+
- fanfiction
|
| 18 |
+
- small-language-model
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| 19 |
+
- tiny
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| 20 |
+
- edge-deployment
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| 21 |
+
- from-scratch
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| 22 |
+
- lissa
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| 23 |
+
widget:
|
| 24 |
+
- text: "<system>You are Twilight Sparkle, a studious purple unicorn who loves books.<user>Twilight, I found a strange book in the library.<assistant>"
|
| 25 |
+
example_title: Twilight Sparkle
|
| 26 |
+
- text: "<system>You are Rainbow Dash, a brash and boastful pegasus who loves speed.<user>Rainbow, are you scared of thunderstorms?<assistant>"
|
| 27 |
+
example_title: Rainbow Dash
|
| 28 |
+
- text: "<system>You are Fluttershy, a shy pegasus who cares for animals.<user>Fluttershy, a bird hurt its wing in my garden.<assistant>"
|
| 29 |
+
example_title: Fluttershy
|
| 30 |
+
model_card_authors:
|
| 31 |
+
- CastIronMind
|
| 32 |
+
model-index:
|
| 33 |
+
- name: LissaNano
|
| 34 |
+
results:
|
| 35 |
+
- task:
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| 36 |
+
type: text-generation
|
| 37 |
+
dataset:
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| 38 |
+
name: Pony domain eval (Fimfarchive, frozen, ~10M tokens)
|
| 39 |
+
type: text
|
| 40 |
+
metrics:
|
| 41 |
+
- name: Perplexity
|
| 42 |
+
type: perplexity
|
| 43 |
+
value: 31.88
|
| 44 |
+
- name: Bits per character
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| 45 |
+
type: bpc
|
| 46 |
+
value: 1.2091
|
| 47 |
+
---
|
| 48 |
+
|
| 49 |
+
<div align="center">
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| 50 |
+
|
| 51 |
+
# 🦄 LissaNano
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| 52 |
+
|
| 53 |
+

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| 54 |
+

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| 55 |
+

|
| 56 |
+

|
| 57 |
+

|
| 58 |
+

|
| 59 |
+

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| 60 |
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|
| 61 |
+
[](https://huggingface.co/CastIronMind)
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| 62 |
+
|
| 63 |
+
</div>
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| 64 |
+
|
| 65 |
+
> ✨ **Самая маленькая RP-модель по My Little Pony, которую мы смогли найти.** 32 миллиона параметров. 64 мегабайта весов. Запускается на телефоне, на Raspberry Pi и на ноутбуке без видеокарты.
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| 66 |
+
|
| 67 |
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> 🧠 **Обучена с нуля, а не урезана из чужой.** Архитектура Llama, словарь 16 384 токена, контекст 1024. Ни одного заимствованного веса: сначала предобучение на фандомном корпусе, потом дообучение на ролёвых диалогах.
|
| 68 |
+
|
| 69 |
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---
|
| 70 |
+
|
| 71 |
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## Что это
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| 72 |
+
|
| 73 |
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**LissaNano** — младшая модель семейства [Lissa](https://huggingface.co/CastIronMind). Она умеет одно, зато честно: держать персонажа и отвечать в его голосе.
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| 74 |
+
|
| 75 |
+
Это не ассистент. Она не ответит на вопрос про капитал Франции и не напишет код. Она нужна для другого — чтобы Twilight Sparkle снова заговорила как Twilight Sparkle.
|
| 76 |
+
|
| 77 |
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**Что она делает хорошо:**
|
| 78 |
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- держит заданную персону и узнаваемый голос персонажа
|
| 79 |
+
- знает лор: Понивилль, Эквестрия, Пегас, Селестия, кьютимарки
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| 80 |
+
- пишет от первого лица, с действиями в `*звёздочках*`
|
| 81 |
+
- работает там, где большие модели не запустятся вообще
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| 82 |
+
|
| 83 |
+
---
|
| 84 |
+
|
| 85 |
+
## Семейство Lissa
|
| 86 |
+
|
| 87 |
+
| модель | параметров | весов | для чего |
|
| 88 |
+
|---|---:|---:|---|
|
| 89 |
+
| **LissaNano** | 32M | 64 МБ | телефон, микрокомпьютер, игрушки, встраивание |
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| 90 |
+
| [LissaBrat](https://huggingface.co/CastIronMind/LissaBrat) | 103M | 206 МБ | ноутбук, лучший связный текст |
|
| 91 |
+
| [Lissa 1.5B](https://huggingface.co/CastIronMind/Lissa-1.5B-Instruct) | 1.45B | 2.9 ГБ | полноценный ролёвый партнёр |
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| 92 |
+
|
| 93 |
+
---
|
| 94 |
+
|
| 95 |
+
## Быстрый старт
|
| 96 |
+
|
| 97 |
+
```python
|
| 98 |
+
import torch
|
| 99 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 100 |
+
|
| 101 |
+
model_id = "CastIronMind/LissaNano"
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| 102 |
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tok = AutoTokenizer.from_pretrained(model_id)
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| 103 |
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
|
| 104 |
+
model.eval()
|
| 105 |
+
|
| 106 |
+
SYS, USR, AST, EOS = 2, 3, 4, 1
|
| 107 |
+
|
| 108 |
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system = "You are Twilight Sparkle, a studious purple unicorn who loves books."
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| 109 |
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user = "Twilight, I found a strange book in the library."
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| 110 |
+
|
| 111 |
+
ids = [SYS] + tok.encode(system, add_special_tokens=False) \
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| 112 |
+
+ [USR] + tok.encode(user, add_special_tokens=False) + [AST]
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| 113 |
+
x = torch.tensor([ids], dtype=torch.long)
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| 114 |
+
|
| 115 |
+
out = model.generate(x, max_new_tokens=120, do_sample=True,
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| 116 |
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temperature=0.8, top_p=0.9, repetition_penalty=1.1,
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| 117 |
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pad_token_id=0, eos_token_id=EOS)
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| 118 |
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print(tok.decode(out[0][len(ids):], skip_special_tokens=True))
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| 119 |
+
```
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| 120 |
+
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| 121 |
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**Формат промпта.** Ровно такой, на котором модель училась:
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| 122 |
+
|
| 123 |
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```
|
| 124 |
+
<system>{персона}<user>{реплика}<assistant>{ответ}<eos>
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| 125 |
+
```
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| 126 |
+
|
| 127 |
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Спецтокены: `0 <pad>`, `1 <eos>`, `2 <system>`, `3 <user>`, `4 <assistant>`.
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| 128 |
+
|
| 129 |
+
**Настройки генерации.** `temperature 0.8`, `top_p 0.9`, `repetition_penalty 1.1`. Температуру выше 1.0 лучше не поднимать — модель маленькая и начинает петлять.
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| 130 |
+
|
| 131 |
+
---
|
| 132 |
+
|
| 133 |
+
## Данные
|
| 134 |
+
|
| 135 |
+
Предобучение: фандомная проза и форумы по My Little Pony — около **1 млрд уникальных токенов** (2 эпохи).
|
| 136 |
+
|
| 137 |
+
Дообучение: **104 732** диалога из общего микса, затем **84 681** диалог только из ролёвых и персональных подмножеств — две эпохи на финальной стадии.
|
| 138 |
+
|
| 139 |
+
Оценка качества: фиксированный набор из 313 текстов Fimfarchive, ~10 млн токенов, не пересекается с обучением.
|
| 140 |
+
|
| 141 |
+
| метрика | значение |
|
| 142 |
+
|---|---:|
|
| 143 |
+
| Perplexity | **31.88** |
|
| 144 |
+
| Bits per character | **1.2091** |
|
| 145 |
+
|
| 146 |
+
---
|
| 147 |
+
|
| 148 |
+
## Железо и цена
|
| 149 |
+
|
| 150 |
+
| | |
|
| 151 |
+
|---|---|
|
| 152 |
+
| Обучение | одна потребительская видеокарта |
|
| 153 |
+
| Автор | один человек |
|
| 154 |
+
| Лицензия | Apache 2.0 |
|
| 155 |
+
| Вес | 64 МБ в bf16 |
|
| 156 |
+
|
| 157 |
+
Никаких кластеров, никаких спонсоров. Модель собрана целиком и полностью руками.
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| 158 |
+
|
| 159 |
+
---
|
| 160 |
+
|
| 161 |
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## Для чего использовать
|
| 162 |
+
|
| 163 |
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- ролёвые боты и персонажи в чатах
|
| 164 |
+
- встраиваемые приложения: игры, визуальные новеллы, тамагочи
|
| 165 |
+
- эксперименты с маленькими языковыми моделями
|
| 166 |
+
- обучение: живой пример полного цикла «с нуля до чат-модели» на одном GPU
|
| 167 |
+
- дальнейший файнтюн под своего персонажа — 32M дообучается за минуты
|
| 168 |
+
|
| 169 |
+
---
|
| 170 |
+
|
| 171 |
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## Лицензия
|
| 172 |
+
|
| 173 |
+
Apache 2.0. Берите, дообучайте, квантуйте, встраивайте, публикуйте свои версии.
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config.json
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{
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| 2 |
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"architectures": [
|
| 3 |
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"LlamaForCausalLM"
|
| 4 |
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],
|
| 5 |
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"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 1,
|
| 8 |
+
"dtype": "bfloat16",
|
| 9 |
+
"eos_token_id": 1,
|
| 10 |
+
"head_dim": 32,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 512,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 1536,
|
| 15 |
+
"max_position_embeddings": 1024,
|
| 16 |
+
"mlp_bias": false,
|
| 17 |
+
"model_type": "llama",
|
| 18 |
+
"num_attention_heads": 16,
|
| 19 |
+
"num_hidden_layers": 8,
|
| 20 |
+
"num_key_value_heads": 2,
|
| 21 |
+
"pad_token_id": 0,
|
| 22 |
+
"pretraining_tp": 1,
|
| 23 |
+
"rms_norm_eps": 1e-06,
|
| 24 |
+
"rope_parameters": {
|
| 25 |
+
"rope_theta": 10000.0,
|
| 26 |
+
"rope_type": "default"
|
| 27 |
+
},
|
| 28 |
+
"tie_word_embeddings": true,
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| 29 |
+
"transformers_version": "5.16.1",
|
| 30 |
+
"use_cache": false,
|
| 31 |
+
"vocab_size": 16384
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| 32 |
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}
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generation_config.json
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{
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| 2 |
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"_from_model_config": true,
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| 3 |
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"bos_token_id": 1,
|
| 4 |
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"eos_token_id": 1,
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| 5 |
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"pad_token_id": 0,
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| 6 |
+
"transformers_version": "5.16.1",
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| 7 |
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"use_cache": true
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| 8 |
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}
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model.safetensors
ADDED
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:f78f3ba85928dc73affe781c65a9b0c792efd4531661251c807c01b147c5bfb9
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| 3 |
+
size 63988672
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,5 @@
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| 1 |
+
{
|
| 2 |
+
"pad_token": "<pad>",
|
| 3 |
+
"eos_token": "<eos>",
|
| 4 |
+
"bos_token": "<eos>"
|
| 5 |
+
}
|
tokenizer.json
ADDED
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,52 @@
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|
| 1 |
+
{
|
| 2 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 3 |
+
"model_max_length": 1024,
|
| 4 |
+
"padding_side": "right",
|
| 5 |
+
"truncation_side": "right",
|
| 6 |
+
"pad_token": "<pad>",
|
| 7 |
+
"eos_token": "<eos>",
|
| 8 |
+
"bos_token": "<eos>",
|
| 9 |
+
"clean_up_tokenization_spaces": false,
|
| 10 |
+
"added_tokens_decoder": {
|
| 11 |
+
"0": {
|
| 12 |
+
"content": "<pad>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"1": {
|
| 20 |
+
"content": "<eos>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"2": {
|
| 28 |
+
"content": "<system>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"3": {
|
| 36 |
+
"content": "<user>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"4": {
|
| 44 |
+
"content": "<assistant>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
}
|
| 51 |
+
}
|
| 52 |
+
}
|