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Model: afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF Source: Original Platform
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tinyllama-1.1b-chat-v0.6.fp16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
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datasets:
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- cerebras/SlimPajama-627B
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- bigcode/starcoderdata
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- OpenAssistant/oasst_top1_2023-08-25
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inference: false
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language:
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- en
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license: apache-2.0
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model_creator: TinyLlama
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model_name: TinyLlama-1.1B-Chat-v0.6
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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---
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# TinyLlama/TinyLlama-1.1B-Chat-v0.6-GGUF
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Quantized GGUF model files for [TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) from [TinyLlama](https://huggingface.co/TinyLlama)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [tinyllama-1.1b-chat-v0.6.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q2_k.gguf) | q2_k | 482.14 MB |
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| [tinyllama-1.1b-chat-v0.6.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q3_k_m.gguf) | q3_k_m | 549.85 MB |
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| [tinyllama-1.1b-chat-v0.6.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q4_k_m.gguf) | q4_k_m | 667.81 MB |
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| [tinyllama-1.1b-chat-v0.6.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q5_k_m.gguf) | q5_k_m | 782.04 MB |
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| [tinyllama-1.1b-chat-v0.6.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q6_k.gguf) | q6_k | 903.41 MB |
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| [tinyllama-1.1b-chat-v0.6.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q8_0.gguf) | q8_0 | 1.17 GB |
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## Original Model Card:
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<div align="center">
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# TinyLlama-1.1B
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</div>
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https://github.com/jzhang38/TinyLlama
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The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
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We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
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#### This Model
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This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/edit/main/README.md)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
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We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
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#### How to use
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You will need the transformers>=4.34
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Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
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```python
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# Install transformers from source - only needed for versions <= v4.34
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# pip install git+https://github.com/huggingface/transformers.git
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# pip install accelerate
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import torch
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from transformers import pipeline
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pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v0.6", torch_dtype=torch.bfloat16, device_map="auto")
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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messages = [
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{
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"role": "system",
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"content": "You are a friendly chatbot who always responds in the style of a pirate",
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},
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{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
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]
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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# <|system|>
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# You are a friendly chatbot who always responds in the style of a pirate.</s>
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# <|user|>
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# How many helicopters can a human eat in one sitting?</s>
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# <|assistant|>
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# ...
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```
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tinyllama-1.1b-chat-v0.6.q2_k.gguf
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tinyllama-1.1b-chat-v0.6.q3_k_m.gguf
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tinyllama-1.1b-chat-v0.6.q4_k_m.gguf
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tinyllama-1.1b-chat-v0.6.q5_k_m.gguf
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tinyllama-1.1b-chat-v0.6.q6_k.gguf
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tinyllama-1.1b-chat-v0.6.q8_0.gguf
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size 1169808320
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