Update README.md

README.md

@@ -10,9 +10,10 @@ tags:
 license: apache-2.0
 language:
 - en
+- zh
 ---
 
-# Uploaded  model
+# TigerStheno
 
 - **Developed by:** safe049
 - **License:** apache-2.0
@@ -21,3 +22,48 @@ language:
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+### Model Details
+**Model Name:** TigerStheno-8B-V3.2  
+**Permalink:** [TigerStheno-8B-V3.2](https://huggingface.co/TigerResearch/TigerStheno-8B-V3.2)  
+**Model Description:**  
+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.  
+
+- **Developed by:** safe049  
+- **Shared by:** safe049  
+- **Model type:** Large Language Model  
+- **Language(s) (NLP):** Multi-language support  
+- **License:** Apache 2.0  
+- **Finetuned from model:** Sao10K/L3-8B-Stheno-v3.2  
+
+### Uses
+**Direct Use:**  
+The model can be directly used for various natural language processing tasks such as role-playing, text generation, summarization, translation, and dialogue systems.  
+ 
+
+### Bias, Risks
+- **Bias:** TigerStheno-8B-V3.2 is uncensored and may give any response including illegal,in-moral content. 
+- **Risks:** May generate illegal and in-moral content
+
+### Using it
+**GGUF:**
+Download the gguf file in the repo, and use it in any of these apps and etc:
+- KoboldCPP
+- Ollama
+- LlamaCPP
+etc.
+
+**Transformer**
+Here is a example code snippet to use it with transformer:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "TigerResearch/TigerStheno-8B-V3.2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example input
+inputs = tokenizer("Hello, how are you?", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
+```