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README.md

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+---
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.4
+datasets:
+- cerebras/SlimPajama-627B
+- bigcode/starcoderdata
+- OpenAssistant/oasst_top1_2023-08-25
+inference: false
+language:
+- en
+license: apache-2.0
+model_creator: TinyLlama
+model_name: TinyLlama-1.1B-Chat-v0.4
+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
+---
+# TinyLlama/TinyLlama-1.1B-Chat-v0.4-GGUF
+
+Quantized GGUF model files for [TinyLlama-1.1B-Chat-v0.4](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4) from [TinyLlama](https://huggingface.co/TinyLlama)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [tinyllama-1.1b-chat-v0.4.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q2_k.gguf) | q2_k | 482.15 MB  |
+| [tinyllama-1.1b-chat-v0.4.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q3_k_m.gguf) | q3_k_m | 549.85 MB  |
+| [tinyllama-1.1b-chat-v0.4.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q4_k_m.gguf) | q4_k_m | 667.82 MB  |
+| [tinyllama-1.1b-chat-v0.4.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q5_k_m.gguf) | q5_k_m | 782.05 MB  |
+| [tinyllama-1.1b-chat-v0.4.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q6_k.gguf) | q6_k | 903.42 MB  |
+| [tinyllama-1.1b-chat-v0.4.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-Chat-v0.4-GGUF/resolve/main/tinyllama-1.1b-chat-v0.4.q8_0.gguf) | q8_0 | 1.17 GB  |
+
+
+
+## Original Model Card:
+<div align="center">
+
+# TinyLlama-1.1B
+</div>
+
+https://github.com/jzhang38/TinyLlama
+
+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. 
+
+
+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.
+
+#### This Model
+This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T). 
+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.
+#### How to use
+You will need the transformers>=4.31
+Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
+```
+from transformers import AutoTokenizer
+import transformers 
+import torch
+model = "PY007/TinyLlama-1.1B-Chat-v0.4"
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+CHAT_EOS_TOKEN_ID = 32002
+
+prompt = "How to get in a good university?"
+formatted_prompt = (
+    f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+)
+
+
+sequences = pipeline(
+    formatted_prompt,
+    do_sample=True,
+    top_k=50,
+    top_p = 0.9,
+    num_return_sequences=1,
+    repetition_penalty=1.1,
+    max_new_tokens=1024,
+    eos_token_id=CHAT_EOS_TOKEN_ID,
+)
+
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```