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Model: OpenAssistant/galactica-6.7b-finetuned
Source: Original Platform

README.md

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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'))
+```