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README.md

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+---
+base_model: Felladrin/Llama-160M-Chat-v1
+datasets:
+- ehartford/wizard_vicuna_70k_unfiltered
+- totally-not-an-llm/EverythingLM-data-V3
+- Open-Orca/SlimOrca-Dedup
+- databricks/databricks-dolly-15k
+- THUDM/webglm-qa
+inference: false
+license: other
+model_creator: Felladrin
+model_name: Llama-160M-Chat-v1
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- text-generation
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+widget:
+- text: "<|im_start|>system\nYou are a helpful assistant, who answers with empathy.<|im_end|>\n<|im_start|>user\nGot
+    a question for you!<|im_end|>\n<|im_start|>assistant\nSure! What's it?<|im_end|>\n<|im_start|>user\nWhy
+    do you love cats so much!? \U0001F408<|im_end|>\n<|im_start|>assistant"
+- text: '<|im_start|>system
+
+    You are a helpful assistant who answers user''s questions with empathy.<|im_end|>
+
+    <|im_start|>user
+
+    Who is Mona Lisa?<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>system
+
+    You are a helpful assistant who provides concise responses.<|im_end|>
+
+    <|im_start|>user
+
+    Heya!<|im_end|>
+
+    <|im_start|>assistant
+
+    Hi! How may I help you today?<|im_end|>
+
+    <|im_start|>user
+
+    I need to build a simple website. Where should I start learning about web development?<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>user
+
+    Invited some friends to come home today. Give me some ideas for games to play
+    with them!<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>system
+
+    You are a helpful assistant who answers user''s questions with details and curiosity.<|im_end|>
+
+    <|im_start|>user
+
+    What are some potential applications for quantum computing?<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>system
+
+    You are a helpful assistant who gives creative responses.<|im_end|>
+
+    <|im_start|>user
+
+    Write the specs of a game about mages in a fantasy world.<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>system
+
+    You are a helpful assistant who answers user''s questions with details.<|im_end|>
+
+    <|im_start|>user
+
+    Tell me about the pros and cons of social media.<|im_end|>
+
+    <|im_start|>assistant'
+- text: '<|im_start|>system
+
+    You are a helpful assistant who answers user''s questions with confidence.<|im_end|>
+
+    <|im_start|>user
+
+    What is a dog?<|im_end|>
+
+    <|im_start|>assistant
+
+    A dog is a four-legged, domesticated animal that is a member of the class Mammalia,
+    which includes all mammals. Dogs are known for their loyalty, playfulness, and
+    ability to be trained for various tasks. They are also used for hunting, herding,
+    and as service animals.<|im_end|>
+
+    <|im_start|>user
+
+    What is the color of an apple?<|im_end|>
+
+    <|im_start|>assistant'
+---
+# Felladrin/Llama-160M-Chat-v1-GGUF
+
+Quantized GGUF model files for [Llama-160M-Chat-v1](https://huggingface.co/Felladrin/Llama-160M-Chat-v1) from [Felladrin](https://huggingface.co/Felladrin)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [llama-160m-chat-v1.fp16.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.fp16.gguf) | fp16 | 326.58 MB  |
+| [llama-160m-chat-v1.q2_k.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q2_k.gguf) | q2_k | 77.23 MB  |
+| [llama-160m-chat-v1.q3_k_m.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q3_k_m.gguf) | q3_k_m | 87.54 MB  |
+| [llama-160m-chat-v1.q4_k_m.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q4_k_m.gguf) | q4_k_m | 104.03 MB  |
+| [llama-160m-chat-v1.q5_k_m.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q5_k_m.gguf) | q5_k_m | 119.04 MB  |
+| [llama-160m-chat-v1.q6_k.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q6_k.gguf) | q6_k | 135.00 MB  |
+| [llama-160m-chat-v1.q8_0.gguf](https://huggingface.co/afrideva/Llama-160M-Chat-v1-GGUF/resolve/main/llama-160m-chat-v1.q8_0.gguf) | q8_0 | 174.33 MB  |
+
+
+
+## Original Model Card:
+# A Llama Chat Model of 160M Parameters
+
+- Base model: [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m)
+- Datasets:
+  - [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
+  - [totally-not-an-llm/EverythingLM-data-V3](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3)
+  - [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
+  - [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
+  - [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
+- Availability in other ML formats:
+  - GGUF: [Felladrin/gguf-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/gguf-Llama-160M-Chat-v1)
+  - ONNX: [Felladrin/onnx-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/onnx-Llama-160M-Chat-v1)
+
+## Recommended Prompt Format
+
+The recommended prompt format is as follows:
+
+```
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{user_message}<|im_end|>
+<|im_start|>assistant
+```
+
+## Recommended Inference Parameters
+
+To get the best results, prefer using [contrastive search](https://huggingface.co/docs/transformers/main/en/generation_strategies#contrastive-search) for inference:
+
+```yml
+penalty_alpha: 0.5
+top_k: 5
+```