taoki
/

Swallow-MX-8x7b-NVE-v0.1-qlora-amenokaku-code-adapter

@@ -1,204 +1,74 @@
 ---
 library_name: peft
 base_model: tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Framework versions
 - PEFT 0.9.0

 ---
 library_name: peft
 base_model: tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
+language:
+- ja
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- trl
+- mixtral
+datasets:
+- kunishou/amenokaku-code-instruct
+license_name: mixtral
 ---
+# Uploaded  model
+- **Developed by:** taoki
+- **License:** apache-2.0
+- **Finetuned from model :** tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
+# Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+model_name = "tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, torch_dtype=torch.bfloat16)
+model = PeftModel.from_pretrained(model, "taoki/Swallow-MX-8x7b-NVE-v0.1-qlora-amenokaku-code-adapter")
+prompt="""### Instruction:
+紫式部と清少納言の作風をjsonで出力してください。
+### Response:
+"""
+input_ids = tokenizer.encode(
+    prompt,
+    add_special_tokens=False,
+    return_tensors="pt"
+)
+tokens = model.generate(
+    input_ids.to(device=model.device),
+    max_new_tokens=1024,
+    temperature=0.99,
+    top_p=0.95,
+    do_sample=True,
+)
+out = tokenizer.decode(tokens[0], skip_special_tokens=True)
+print(out)
+```
+# Output
+````
+### Instruction:
+紫式部と清少納言の作風をjsonで出力してください。
+### Response:
+```json
+{
+    "紫式部": "貴人に会って、その人が話していることを思い出しながら奏でると、これにまさる楽器はありません。」,
+    "清少納言": "人によってあげくはなく、おのずからかなしくゆくほどに、かなしみは深くなりゆきなさるなり。」
+}
+```
+````
+# Framework versions
 - PEFT 0.9.0