wwewtech/russian-it-community-lora

🦁 Russian IT Community LoRA Model Zoo

58 pre-trained adapters (55 domain adapters + 3 flagship 7B–8B QLoRA), fine-tuned on the RICC corpus (2.91M messages, 171.5k curated SFT dialogues) for Russian-language IT discourse: backend, DevOps, AI/ML, infrastructure.

Catalog regenerated from the Hub file tree on 2026-08-26. Source of truth: the siblings listing of this repository.

⚡ Quick Start: 3-Line Inference

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen2.5-1.5B-Instruct"          # any base model from the catalog
adapter_id = "wwewtech/russian-it-community-lora"
subfolder = "qwen2.5_1.5b_instruct"              # choose from the catalog below

tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id, subfolder=subfolder)

inputs = tokenizer("<|user|>\nКак настроить репликацию PostgreSQL?<|assistant|>\n", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📚 Full Catalog (58 Adapters)

#	Adapter Subfolder	Base Model	Link
01	bloom_1b7	bigscience/bloom-1b7	bloom_1b7/
02	bloom_560m	bigscience/bloom-560m	bloom_560m/
03	codegen_350m_multi	Salesforce/codegen-350M-multi	codegen_350m_multi/
04	deepseek_coder_1.3b_instruct	deepseek-ai/deepseek-coder-1.3b-instruct	deepseek_coder_1.3b_instruct/
05	deepseek_r1_distill_qwen_1.5b	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	deepseek_r1_distill_qwen_1.5b/
06	falcon3_1b_instruct	tiiuae/Falcon3-1B-Instruct	falcon3_1b_instruct/
07	falcon3_3b_instruct	tiiuae/Falcon3-3B-Instruct	falcon3_3b_instruct/
08	gemma_2_2b_it	unsloth/gemma-2-2b-it	gemma_2_2b_it/
09	gpt2_large	openai-community/gpt2-large	gpt2_large/
10	gpt2_medium	openai-community/gpt2-medium	gpt2_medium/
11	granite_3b_code_instruct	ibm-granite/granite-3b-code-instruct	granite_3b_code_instruct/
12	heavyweight_deepseek_r1_7b	unsloth/DeepSeek-R1-Distill-Qwen-7B-bnb-4bit	heavyweight_deepseek_r1_7b/
13	heavyweight_llama3.1_8b	unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit	heavyweight_llama3.1_8b/
14	heavyweight_qwen2.5_coder_7b	unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit	heavyweight_qwen2.5_coder_7b/
15	llama_3.2_1b_instruct	unsloth/Llama-3.2-1B-Instruct	llama_3.2_1b_instruct/
16	llama_3.2_3b_instruct	unsloth/Llama-3.2-3B-Instruct	llama_3.2_3b_instruct/
17	minicpm_2b_dpo	openbmb/MiniCPM-2B-dpo-bf16	minicpm_2b_dpo/
18	opt_1.3b	facebook/opt-1.3b	opt_1.3b/
19	opt_125m	facebook/opt-125m	opt_125m/
20	opt_2.7b	facebook/opt-2.7b	opt_2.7b/
21	opt_350m	facebook/opt-350m	opt_350m/
22	phi_1_5	microsoft/phi-1_5	phi_1_5/
23	phi_2	microsoft/phi-2	phi_2/
24	phi_3.5_mini_instruct	microsoft/Phi-3.5-mini-instruct	phi_3.5_mini_instruct/
25	phi_3_mini_4k_instruct	microsoft/Phi-3-mini-4k-instruct	phi_3_mini_4k_instruct/
26	pythia_1.4b	EleutherAI/pythia-1.4b-deduped	pythia_1.4b/
27	pythia_2.8b	EleutherAI/pythia-2.8b-deduped	pythia_2.8b/
28	pythia_410m	EleutherAI/pythia-410m-deduped	pythia_410m/
29	pythia_70m	EleutherAI/pythia-70m-deduped	pythia_70m/
30	qwen1.5_0.5b_chat	Qwen/Qwen1.5-0.5B-Chat	qwen1.5_0.5b_chat/
31	qwen1.5_1.8b_chat	Qwen/Qwen1.5-1.8B-Chat	qwen1.5_1.8b_chat/
32	qwen2.5_0.5b_instruct	Qwen/Qwen2.5-0.5B-Instruct	qwen2.5_0.5b_instruct/
33	qwen2.5_1.5b_instruct	Qwen/Qwen2.5-1.5B-Instruct	qwen2.5_1.5b_instruct/
34	qwen2.5_3b_instruct	Qwen/Qwen2.5-3B-Instruct	qwen2.5_3b_instruct/
35	qwen2.5_coder_0.5b_instruct	Qwen/Qwen2.5-Coder-0.5B-Instruct	qwen2.5_coder_0.5b_instruct/
36	qwen2.5_coder_1.5b_instruct	Qwen/Qwen2.5-Coder-1.5B-Instruct	qwen2.5_coder_1.5b_instruct/
37	qwen2.5_coder_3b_instruct	Qwen/Qwen2.5-Coder-3B-Instruct	qwen2.5_coder_3b_instruct/
38	qwen2.5_math_1.5b_instruct	Qwen/Qwen2.5-Math-1.5B-Instruct	qwen2.5_math_1.5b_instruct/
39	qwen2_0.5b_instruct	Qwen/Qwen2-0.5B-Instruct	qwen2_0.5b_instruct/
40	qwen2_1.5b_instruct	Qwen/Qwen2-1.5B-Instruct	qwen2_1.5b_instruct/
41	rugpt3_large	ai-forever/rugpt3large_based_on_gpt2	rugpt3_large/
42	rugpt3_medium	ai-forever/rugpt3medium_based_on_gpt2	rugpt3_medium/
43	rugpt3_small	ai-forever/rugpt3small_based_on_gpt2	rugpt3_small/
44	russian_it_lora	Qwen/Qwen2.5-0.5B-Instruct	russian_it_lora/
45	sber_mgpt	ai-forever/mGPT	sber_mgpt/
46	smollm2_1.7b_instruct	HuggingFaceTB/SmolLM2-1.7B-Instruct	smollm2_1.7b_instruct/
47	smollm2_135m_instruct	HuggingFaceTB/SmolLM2-135M-Instruct	smollm2_135m_instruct/
48	smollm2_360m_instruct	HuggingFaceTB/SmolLM2-360M-Instruct	smollm2_360m_instruct/
49	smollm_1.7b_instruct	HuggingFaceTB/SmolLM-1.7B-Instruct	smollm_1.7b_instruct/
50	smollm_135m_instruct	HuggingFaceTB/SmolLM-135M-Instruct	smollm_135m_instruct/
51	smollm_360m_instruct	HuggingFaceTB/SmolLM-360M-Instruct	smollm_360m_instruct/
52	stablelm_2_1_6b_chat	stabilityai/stablelm-2-1_6b-chat	stablelm_2_1_6b_chat/
53	stablelm_2_zephyr_1_6b	stabilityai/stablelm-2-zephyr-1_6b	stablelm_2_zephyr_1_6b/
54	tiny_starcoder_py	bigcode/tiny_starcoder_py	tiny_starcoder_py/
55	tinyllama_1.1b_chat	TinyLlama/TinyLlama-1.1B-Chat-v1.0	tinyllama_1.1b_chat/
56	vikhr_llama_3.2_1b	Vikhrmodels/Vikhr-Llama-3.2-1B-instruct	vikhr_llama_3.2_1b/
57	vikhr_qwen_2.5_0.5b	Vikhrmodels/Vikhr-Qwen-2.5-0.5B-Instruct	vikhr_qwen_2.5_0.5b/
58	vikhr_qwen_2.5_1.5b	Vikhrmodels/Vikhr-Qwen-2.5-1.5B-Instruct	vikhr_qwen_2.5_1.5b/

🥇 Flagship QLoRA Models (7B–8B)

Full-precision copies also live under models/: models/heavyweight_qwen2.5_coder_7b, models/heavyweight_deepseek_r1_7b, models/heavyweight_llama3.1_8b.

📓 Training Data & Evaluation Status

• Training corpus: RICC SFT Dialogues (171,520 multi-turn dialogues).
• Academic benchmark numbers (HumanEval / RuMMLU / PPL) published earlier are withdrawn pending re-evaluation: the harness had answer-parsing and column-mapping defects that produced implausible values (see repo commit history). Enterprise scenario scores are rubric-based heuristics, not capability measurements.