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--- |
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base_model: Sao10K/L3-8B-Stheno-v3.2 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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- sft |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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--- |
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# TigerStheno |
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- **Developed by:** safe049 |
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- **License:** apache-2.0 |
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- **Finetuned from model :** Sao10K/L3-8B-Stheno-v3.2 |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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### Model Details |
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**Model Name:** TigerStheno-8B-V3.2 |
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**Permalink:** [TigerStheno-8B-V3.2](https://huggingface.co/TigerResearch/TigerStheno-8B-V3.2) |
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**Model Description:** |
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TigerStheno-8B-V3.2 is an AI model based on the Sao10K/L3-8B-Stheno-v3.2 architecture, fine-tuned using the TigerBot dataset. This model is designed to enhance language understanding and generation capabilities, particularly in multi-language and multi-task role-playing scenarios. |
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- **Developed by:** safe049 |
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- **Shared by:** safe049 |
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- **Model type:** Large Language Model |
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- **Language(s) (NLP):** Multi-language support |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** Sao10K/L3-8B-Stheno-v3.2 |
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### Uses |
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**Direct Use:** |
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The model can be directly used for various natural language processing tasks such as role-playing, text generation, summarization, translation, and dialogue systems. |
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### Bias, Risks |
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- **Bias:** TigerStheno-8B-V3.2 is uncensored and may give any response including illegal,in-moral content. |
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- **Risks:** May generate illegal and in-moral content |
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### Using it |
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**GGUF:** |
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Download the gguf file in the repo, and use it in any of these apps and etc: |
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- KoboldCPP |
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- Ollama |
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- LlamaCPP |
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etc. |
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**Transformer** |
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Here is a example code snippet to use it with transformer: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "TigerResearch/TigerStheno-8B-V3.2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Example input |
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inputs = tokenizer("Hello, how are you?", return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
