Initial run with default params

README.md

@@ -1,3 +1,83 @@
 ---
+language:
+  - en
+library_name: peft
+tags:
+  - llama
+  - lora
+  - peft
 license: apache-2.0
 ---
+
+[Low-Rank-Adaption (LoRA)](https://paperswithcode.com/paper/lora-low-rank-adaptation-of-large-language) of [LLAMA 6B model](https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1) that is fine-tuned with [Stanford Alpaca instruction dataset](https://github.com/tatsu-lab/stanford_alpaca) using [PEFT](https://github.com/huggingface/peft).
+
+This model is trained based on the script provided in https://github.com/tloen/alpaca-lora.
+
+> You might need to install the latest transformers from github for Llama support.
+
+```python
+from peft import PeftModel
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+
+model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(
+    model, "tloen/alpaca-lora-7b",
+    torch_dtype=torch.float16
+)
+
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{input}
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Response:"""
+
+
+model.eval()
+
+
+def evaluate(
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs,
+):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(device)
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=2048,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].strip()
+```

adapter_config.json

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+{
+  "base_model_name_or_path": "decapoda-research/llama-7b-hf",
+  "bias": "none",
+  "enable_lora": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "lora_alpha": 16,
+  "lora_dropout": 0.05,
+  "merge_weights": false,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a734191cc72b69cced6f1a99a27954e90f384646d9dfc5844423d24518d1da98
+size 16822989