95 lines
3.6 KiB
Markdown
95 lines
3.6 KiB
Markdown
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---
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.5
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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-v0.5
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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-v0.5-GGUF
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Quantized GGUF model files for [TinyLlama-1.1B-Chat-v0.5](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.5) 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-v0.5.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.q2_k.gguf) | q2_k | 482.15 MB |
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| [tinyllama-1.1b-chat-v0.5.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.q3_k_m.gguf) | q3_k_m | 549.85 MB |
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| [tinyllama-1.1b-chat-v0.5.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.q4_k_m.gguf) | q4_k_m | 667.82 MB |
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| [tinyllama-1.1b-chat-v0.5.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.q5_k_m.gguf) | q5_k_m | 782.05 MB |
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| [tinyllama-1.1b-chat-v0.5.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.q6_k.gguf) | q6_k | 903.42 MB |
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| [tinyllama-1.1b-chat-v0.5.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.5-GGUF/resolve/main/tinyllama-1.1b-chat-v0.5.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).
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The dataset used is [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25) following the [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) format.
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#### How to use
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You will need the transformers>=4.31
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Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
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```
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "PY007/TinyLlama-1.1B-Chat-v0.5"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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CHAT_EOS_TOKEN_ID = 32002
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prompt = "How to get in a good university?"
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formatted_prompt = (
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f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
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)
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sequences = pipeline(
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formatted_prompt,
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do_sample=True,
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top_k=50,
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top_p = 0.9,
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num_return_sequences=1,
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repetition_penalty=1.1,
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max_new_tokens=1024,
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eos_token_id=CHAT_EOS_TOKEN_ID,
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)
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for seq in sequences:
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print(f"Result: {seq['generated_text']}")
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```
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