初始化项目,由ModelHub XC社区提供模型

Model: Bhooyas/tinyllama-dolly-15k
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-08-06 08:27:17 +08:00
commit 8956b2d7f9
8 changed files with 93576 additions and 0 deletions

37
README.md Normal file
View File

@@ -0,0 +1,37 @@
---
license: apache-2.0
datasets:
- databricks/databricks-dolly-15k
language:
- en
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
---
# TinyLlama Dolly 15
The TinyLlama Dolly 15k is a specialized large language model finely tuned on the Databricks Dolly 15k dataset. This dataset, composed of 15,000 high-quality, human-curated prompts and responses, helps the model excel in understanding and generating contextually relevant and coherent text. By leveraging this diverse and comprehensive dataset, TinyLlama Dolly 15k enhances its ability to engage in nuanced conversations and provide accurate, context-aware responses across a range of topics. The result is a powerful tool for applications requiring advanced natural language understanding and generation.
**How to use**
Below is a snippet that can be used to test the model.
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="Bhooyas/tinyllama-dolly-15k", device_map="auto")
messages = [
{
"role": "user",
"context": "The TinyLlama Dolly 15k is a specialized large language model finely tuned on the Databricks Dolly 15k dataset. This dataset, composed of 15,000 high-quality, human-curated prompts and responses, helps the model excel in understanding and generating contextually relevant and coherent text. By leveraging this diverse and comprehensive dataset, TinyLlama Dolly 15k enhances its ability to engage in nuanced conversations and provide accurate, context-aware responses across a range of topics. The result is a powerful tool for applications requiring advanced natural language understanding and generation.",
"instruction": "What is TinyLlama Dolly 15k?"
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256)
print(outputs[0]["generated_text"])
```