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5.5bpw/.gitattributes

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5.5bpw/README.md

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+---
+library_name: transformers
+language:
+- ru
+- en
+license: apache-2.0
+---
+
+# Релиз вихря 0.5*
+
+Долили сильно больше данных в sft, теперь стабильнее работает json и multiturn, слегка подточили параметры претрена модели, добавили RoPE на 32к контекста
+
+Added a lot more data to sft, now json and multiturn work more stable on long context and hard prompts
+
+ - [Google Colab] - later
+ - [GGUF](https://huggingface.co/Vikhrmodels/it-5.3-fp16-32k-GGUF)
+
+```python
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/it-5.3-fp16-32k",
+                                             device_map="auto",
+                                             attn_implementation="sdpa",
+                                             torch_dtype=torch.bfloat16)
+
+tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/it-5.3-fp16-32k")
+from transformers import  AutoTokenizer, pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+prompts = [
+    "В чем разница между фруктом и овощем?",
+    "Годы жизни колмагорова?"]
+
+def test_inference(prompt):
+    prompt = pipe.tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True)
+    print(prompt)
+    outputs = pipe(prompt, max_new_tokens=512, do_sample=True, num_beams=1, temperature=0.25, top_k=50, top_p=0.98, eos_token_id=79097)
+    return outputs[0]['generated_text'][len(prompt):].strip()
+
+
+for prompt in prompts:
+    print(f"    prompt:\n{prompt}")
+    print(f"    response:\n{test_inference(prompt)}")
+    print("-"*50)
+
+```
+
+
+```
+
+@article{nikolich2024vikhr,
+  title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
+  author={Aleksandr Nikolich and Konstantin Korolev and Artem Shelmanov},
+  journal={arXiv preprint arXiv:2405.13929},
+  year={2024},
+  url={https://arxiv.org/pdf/2405.13929}
+}
+```

5.5bpw/added_tokens.json

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+  "<|future_token_10|>": 79095,
+  "<|future_token_11|>": 79096,
+  "<|future_token_1|>": 79086,
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+  "<|future_token_7|>": 79092,
+  "<|future_token_8|>": 79093,
+  "<|future_token_9|>": 79094
+}

5.5bpw/config.json

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+{
+    "_name_or_path": "model_cp",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pad_token_id": 2,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "sliding_window": 4096,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float16",
+    "transformers_version": "4.41.1",
+    "use_cache": false,
+    "vocab_size": 79097,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.5",
+        "bits": 5.5,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 100,
+            "length": 32000,
+            "dataset": "data"
+        }
+    }
+}

5.5bpw/generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": 79097,
+  "max_new_tokens": 1024,
+  "num_beams": 3,
+  "pad_token_id": 2,
+  "temperature": 0.2,
+  "top_p": 0.98,
+  "transformers_version": "4.41.1",
+  "use_cache": false
+}

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