File size: 1,659 Bytes
75e394f a7da558 55da335 75e394f f36e29f 75e394f a87bee4 058d7b4 f1c41f6 632e2c6 a87bee4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
library_name: transformers
tags:
- trl
- sft
datasets:
- Vikhrmodels/Veles-2.5
- dichspace/darulm
- zjkarina/Vikhr_instruct
---
# Veles Instruct [DONT TOUCH, Under Dev]
Просто лучшая русская инстракт модель
Метрки, DPO, коды для запуска подьедут позже
используйте transformers==4.36.2 и будет счастье
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.3",
device_map="auto",
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.3",use_fast=False)
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=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, eos_token_id=tokenizer.eos_token_id)
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)
``` |