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README.md

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+
+---
+
+language:
+- ru
+datasets:
+- IlyaGusev/saiga_scored
+- IlyaGusev/saiga_preferences
+license: gemma
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/saiga_gemma2_9b-GGUF
+This is quantized version of [IlyaGusev/saiga_gemma2_9b](https://huggingface.co/IlyaGusev/saiga_gemma2_9b) created using llama.cpp
+
+# Original Model Card
+
+
+
+# Saiga/Gemma2 9B, Russian Gemma-2-based chatbot
+
+Based on [Gemma-2 9B Instruct](https://huggingface.co/google/gemma-2-9b-it).
+
+## Prompt format
+
+Gemma-2 prompt format:
+```
+<start_of_turn>system
+Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<end_of_turn>
+<start_of_turn>user
+Как дела?<end_of_turn>
+<start_of_turn>model
+Отлично, а у тебя?<end_of_turn>
+<start_of_turn>user
+Шикарно. Как пройти в библиотеку?<end_of_turn>
+<start_of_turn>model
+```
+
+
+## Code example
+```python
+# Исключительно ознакомительный пример.
+# НЕ НАДО ТАК ИНФЕРИТЬ МОДЕЛЬ В ПРОДЕ.
+# См. https://github.com/vllm-project/vllm или https://github.com/huggingface/text-generation-inference
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+MODEL_NAME = "IlyaGusev/saiga_gemma2_10b"
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    load_in_8bit=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+print(generation_config)
+
+inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
+for query in inputs:
+    prompt = tokenizer.apply_chat_template([{
+        "role": "user",
+        "content": query
+    }], tokenize=False, add_generation_prompt=True)
+    data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+    data = {k: v.to(model.device) for k, v in data.items()}
+    output_ids = model.generate(**data, generation_config=generation_config)[0]
+    output_ids = output_ids[len(data["input_ids"][0]):]
+    output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
+    print(query)
+    print(output)
+    print()
+    print("==============================")
+    print()
+```
+
+
+## Versions
+v2:
+- [258869abdf95aca1658b069bcff69ea6d2299e7f](https://huggingface.co/IlyaGusev/saiga_gemma2_9b/commit/258869abdf95aca1658b069bcff69ea6d2299e7f)
+- Other name: saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated_kto_m1_d13
+- SFT dataset config: [sft_d9.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/sft_d9.json)
+- SFT model config: [saiga_gemma2_9b_sft_m2.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_gemma2_9b_sft_m3.json)
+- KTO dataset config: [pref_d11.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/pref_d13.json)
+- KTO model config: [saiga_gemma2_9b_kto_m1.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_gemma2_9b_kto_m1.json)
+- SFT wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/pjsuik1l)
+- KTO wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/dsxwvyyx)
+
+v1:
+- [fa63cfe898ee6372419b8e38d35f4c41756d2c22](https://huggingface.co/IlyaGusev/saiga_gemma2_9b/commit/fa63cfe898ee6372419b8e38d35f4c41756d2c22)
+- Other name: saiga_gemma2_9b_abliterated_sft_m2_d9_abliterated_kto_m1_d11
+- SFT dataset config: [sft_d9.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/sft_d9.json)
+- SFT model config: [saiga_gemma2_9b_sft_m2.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_gemma2_9b_sft_m2.json)
+- KTO dataset config: [pref_d11.json](https://github.com/IlyaGusev/saiga/blob/main/configs/datasets/pref_d11.json)
+- KTO model config: [saiga_gemma2_9b_kto_m1.json](https://github.com/IlyaGusev/saiga/blob/main/configs/models/saiga_gemma2_9b_kto_m1.json)
+- SFT wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/af49qmbb)
+- KTO wandb: [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/5bt7729x)
+
+## Evaluation
+
+* Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl
+* Framework: https://github.com/tatsu-lab/alpaca_eval
+* Evaluator: alpaca_eval_cot_gpt4_turbo_fn
+
+Pivot: gemma_2_9b_it_abliterated
+| model | length_controlled_winrate |  win_rate | standard_error  | avg_length |
+|-----|-----|-----|-----|-----|
+|gemma_2_9b_it_abliterated | 50.00 | 50.00 | 0.00  | 1126 |
+|saiga_gemma2_9b, v1 | 48.66  | 45.54 | 2.45  | 1066 |
+|saiga_gemms2_9b, v2 | 47.77  | 45.30  | 2.45  |  1074 |