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Model: OpenAssistant/galactica-6.7b-finetuned
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2026-07-09 19:00:12 +08:00
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Galactica-6.7b finetuned on webgpt and prompt_dialogue (version v2)
Demo use:
```
import torch
from torch import nn
from torch.nn import functional as F
import transformers
base_path = 'OpenAssistant/galactica-6.7b-finetuned'
model = transformers.OPTForCausalLM.from_pretrained(
base_path, load_in_8bit=True, device_map='auto', low_cpu_mem_usage=True,
torch_dtype=torch.float16, offload_state_dict=True
)
model.gradient_checkpointing_enable() # reduce number of stored activations
model.model.decoder.project_in = lambda x: x.requires_grad_(True)
class CastOutputToFloat(nn.Sequential):
def forward(self, x): return super().forward(x).to(torch.float32)
model.lm_head = CastOutputToFloat(model.lm_head)
tokenizer = transformers.AutoTokenizer.from_pretrained(base_path)
batch = tokenizer.encode("<question>What are the symptoms of Alzheimer's disease?<answer>", return_tensors="pt")
with torch.cuda.amp.autocast():
out = model.generate(
input_ids=batch.to(model.device),
max_length=300,
do_sample=True,
top_k=40,
num_beams=1,
num_return_sequences=1,
eos_token_id=tokenizer.additional_special_tokens_ids[tokenizer.additional_special_tokens.index('<question>')]
)
print(tokenizer.decode(out[0, :-1]).replace('<question>', "User:\n").replace('<answer>', '\nAssistant:\n'))
```

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}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<question>",
"<answer>"
],
"eos_token": "</s>",
"pad_token": "<pad>"
}

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tokenizer.json Normal file
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6
tokenizer_config.json Normal file
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{
"model_max_length": 1000000000000000019884624838656,
"name_or_path": "/local/home/sanagnos/tmp/checkpoint-2500",
"special_tokens_map_file": "/content/tokenizer/special_tokens_map.json",
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}