Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Locutusque/hercules-v5.0
+base_model: M4-ai/Hercules-5.0-Qwen2-1.5B
+language:
+- en
+inference:
+  parameters:
+    do_sample: true
+    temperature: 0.8
+    top_p: 0.95
+    top_k: 40
+    min_p: 0.1
+    max_new_tokens: 250
+    repetition_penalty: 1.1
+---
+
+
+# Hercules-5.0-Qwen2-1.5B-GGUF
+This is quantized version of [M4-ai/Hercules-5.0-Qwen2-1.5B](https://huggingface.co/M4-ai/Hercules-5.0-Qwen2-1.5B) created using llama.cpp
+
+
+# Model Description
+
+<!-- Provide a quick summary of what the model is/does. -->
+We fine-tuned qwen2-1.5B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format.
+
+
+## Model Details
+
+<!-- Provide a longer summary of what this model is. -->
+
+This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.
+
+- **Developed by:** M4-ai
+- **Language(s) (NLP):** English and maybe Chinese
+- **License:** apache-2.0
+- **Finetuned from model:** [qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+General purpose assistant, question answering, chain-of-thought, etc..
+
+This language model made an impressive achievement, and correctly implemented a Multi Head Attention for use in a transformer neural network.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## Training Details
+
+### Training Data
+
+- Locutusque/hercules-v5.0
+
+## Evaluations
+
+coming soon
+
+#### Training Hyperparameters
+
+- **Training regime:** bf16 non-mixed precision
+## Technical Specifications
+
+#### Hardware
+
+We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536.