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Model: afrideva/tau-0.5B-instruct-GGUF
Source: Original Platform

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README.md

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+---
+base_model: M4-ai/tau-0.5B-instruct
+inference: false
+language:
+- en
+license: other
+model_creator: M4-ai
+model_name: tau-0.5B-instruct
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# M4-ai/tau-0.5B-instruct-GGUF
+
+Quantized GGUF model files for [tau-0.5B-instruct](https://huggingface.co/M4-ai/tau-0.5B-instruct) from [M4-ai](https://huggingface.co/M4-ai)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [tau-0.5b-instruct.fp16.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.fp16.gguf) | fp16 | 1.25 GB  |
+| [tau-0.5b-instruct.q2_k.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q2_k.gguf) | q2_k | 298.41 MB  |
+| [tau-0.5b-instruct.q3_k_m.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q3_k_m.gguf) | q3_k_m | 349.88 MB  |
+| [tau-0.5b-instruct.q4_k_m.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q4_k_m.gguf) | q4_k_m | 407.16 MB  |
+| [tau-0.5b-instruct.q5_k_m.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q5_k_m.gguf) | q5_k_m | 459.24 MB  |
+| [tau-0.5b-instruct.q6_k.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q6_k.gguf) | q6_k | 514.58 MB  |
+| [tau-0.5b-instruct.q8_0.gguf](https://huggingface.co/afrideva/tau-0.5B-instruct-GGUF/resolve/main/tau-0.5b-instruct.q8_0.gguf) | q8_0 | 664.60 MB  |
+
+
+
+## Original Model Card:
+# tau-instruct-0.5B
+
+## Model Details
+- **Model Name:** tau-instruct-0.5B
+- **Base Model:** tau-0.5B
+- **Model Size:** 0.5B parameters
+- **Model Type:** Instruction-following Language Model
+- **Training Data**: About 16,000 entries generated by GPT-4.
+
+## Model Use
+tau-instruct-0.5B is an instruction-following language model designed to follow user instructions and provide assistance across a wide range of tasks, including but not limited to:
+- Question answering
+- Text generation and completion
+- Mathematical problem solving
+- Code understanding, generation, and explanation
+- Reasoning and analysis
+- Trivia and general knowledge
+
+The model's ability to follow instructions, combined with its knowledge in various domains, makes it suitable for applications such as virtual assistants, educational tools, and research aids.
+
+## Performance and Limitations
+Preliminary evaluations indicate that tau-instruct-0.5B exhibits improved performance in following instructions compared to its base model, tau-0.5B. However, the model may still have limitations and biases inherited from its base model and the fine-tuning dataset.
+
+Users should be aware that the model's performance may vary depending on the complexity and clarity of the provided instructions. It is essential to evaluate the model's outputs critically and provide feedback to support ongoing improvements.
+
+## Environmental Impact
+The fine-tuning process for tau-instruct-0.5B required additional computational resources, contributing to the model's overall environmental impact. Efforts were made to optimize the fine-tuning process and minimize the carbon footprint.
+
+## Ethical Considerations
+tau-instruct-0.5B has the potential to be used in a wide range of applications, some of which may have ethical implications. Users should ensure that the model is used responsibly and does not cause harm or discriminate against individuals or groups.
+
+As with any AI system, it is crucial to consider the potential biases and limitations of the model when deploying it in real-world applications.
+
+## Usage Rights
+Make sure to read Qwen's license before using this model. The fine-tuned model, tau-instruct-0.5B, is subject to the same usage rights as its base model, tau-0.5B.
+
+## Evaluation
+Coming soon.

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