Instructions to use Daxstar/TinyLlama-1.1B-python-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Daxstar/TinyLlama-1.1B-python-v0.1", filename="ggml-model-q4_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0 # Run inference directly in the terminal: llama cli -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0 # Run inference directly in the terminal: llama cli -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
Use Docker
docker model run hf.co/Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
- LM Studio
- Jan
- Ollama
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with Ollama:
ollama run hf.co/Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
- Unsloth Studio
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Daxstar/TinyLlama-1.1B-python-v0.1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Daxstar/TinyLlama-1.1B-python-v0.1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Daxstar/TinyLlama-1.1B-python-v0.1 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with Docker Model Runner:
docker model run hf.co/Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
- Lemonade
How to use Daxstar/TinyLlama-1.1B-python-v0.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Daxstar/TinyLlama-1.1B-python-v0.1:Q4_0
Run and chat with the model
lemonade run user.TinyLlama-1.1B-python-v0.1-Q4_0
List all available models
lemonade list
Duplicate from TinyLlama/TinyLlama-1.1B-python-v0.1
Browse filesCo-authored-by: Zhang Peiyuan <PY007@users.noreply.huggingface.co>
- .gitattributes +36 -0
- README.md +28 -0
- config.json +25 -0
- generation_config.json +7 -0
- ggml-model-q4_0.gguf +3 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +35 -0
.gitattributes
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README.md
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---
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license: apache-2.0
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datasets:
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- cerebras/SlimPajama-627B
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- bigcode/starcoderdata
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language:
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- en
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---
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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 a code LM finetuned(or so-called continue pretrianed) from the 500B TinyLlama checkpoint with another 7B Python data from the starcoderdata.
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**While the finetuning data is exclusively Python, the model retains its ability in many other languages such as C or Java**.
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The HumanEval accuracy is **14**.
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**It can be used as the draft model to speculative-decode larger models such as models in the CodeLlama family**.
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config.json
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{
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"_name_or_path": "meta-llama/Llama-2-7b-hf",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0.dev0",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"max_length": 2048,
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"transformers_version": "4.31.0.dev0"
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}
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ggml-model-q4_0.gguf
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version https://git-lfs.github.com/spec/v1
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size 636725696
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pytorch_model.bin
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size 4400253617
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"rstrip": false,
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"single_word": false
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"eos_token": {
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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size 499723
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"clean_up_tokenization_spaces": false,
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"eos_token": {
|
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
|
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"pad_token": null,
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"padding_side": "right",
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| 25 |
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"sp_model_kwargs": {},
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| 26 |
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"tokenizer_class": "LlamaTokenizer",
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| 27 |
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"unk_token": {
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| 28 |
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"__type": "AddedToken",
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| 29 |
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"content": "<unk>",
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| 30 |
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"lstrip": false,
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| 31 |
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"normalized": false,
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"rstrip": false,
|
| 33 |
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"single_word": false
|
| 34 |
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}
|
| 35 |
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}
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