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Model: mobiuslabsgmbh/aanaphi2-v0.1 Source: Original Platform
2026-10-15 17:58:17 +08:00
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+---
+license: mit
+train: false
+inference: false
+pipeline_tag: text-generation
+---
+*aanaphi2-v0.1* is a finetuned (SFT + DPO) chat model based on <a href="https://huggingface.co/microsoft/phi-2">Microsoft's Phi-2 base model</a> (2.8B parameters). 
+
+![image/gif](https://cdn-uploads.huggingface.co/production/uploads/636b945ef575d3705149e982/pIeboaaroFY5fpomUADrS.gif)
+
+## Performance
+| Models            | phi-2            | aanaphi2-v0.1    |
+|-------------------|------------------|------------------|
+| ARC (25-shot)     | 61.09            | <b>63.74</b>     |
+| HellaSwag (10-shot)| 75.11           | <b>78.30</b>     |
+| MMLU (5-shot)     | <b>58.11</b>     | 57.70            |
+| TruthfulQA-MC2    | 44.47            | <b>51.56</b>     |
+| Winogrande (5-shot)| <b>74.35</b>    | 73.40            |
+| GSM8K (5-shot)    | 54.81            | <b>58.61</b>     |
+| Average           | 61.33            | <b>63.89</b>     |
+
+
+## Installation
+Make sure you have the latest version of the transformers library:
+```
+pip install pip --upgrade && pip install transformers --upgrade
+```
+
+## Basic Usage
+``` Python
+#Load model
+import transformers, torch
+
+#GPU runtime
+device        = 'cuda'
+compute_dtype = torch.float16
+
+##CPU runtime
+#device        = 'cpu'
+#compute_dtype = torch.float32
+
+cache_path    = ''
+model_id      = "mobiuslabsgmbh/aanaphi2-v0.1"
+model         = transformers.AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=compute_dtype, 
+                                                                  cache_dir=cache_path,
+                                                                  device_map=device)
+tokenizer     = transformers.AutoTokenizer.from_pretrained(model_id, cache_dir=cache_path)
+
+#Set Prompt format
+instruction_template = "### Human: "
+response_template    = "### Assistant: "
+def prompt_format(prompt):
+    out = instruction_template + prompt + '\n' + response_template
+    return out
+model.eval();
+
+@torch.no_grad()
+def generate(prompt, max_length=1024):
+    prompt_chat = prompt_format(prompt)
+    inputs      = tokenizer(prompt_chat, return_tensors="pt", return_attention_mask=True).to(device)
+    outputs     = model.generate(**inputs, max_length=max_length, eos_token_id= tokenizer.eos_token_id) 
+    text        = tokenizer.batch_decode(outputs[:,:-1])[0]
+    return text
+
+#Generate
+print(generate('If A+B=C and B=C, what would be the value of A?'))
+```