Instructions to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF 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 afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

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 afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

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 afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF 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 afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF 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 afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF to start chatting

Docker Model Runner
How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF

Quantized GGUF model files for TinyLlama-1.1B-intermediate-step-1431k-3T from TinyLlama

Name	Quant method	Size
tinyllama-1.1b-intermediate-step-1431k-3t.fp16.gguf	fp16	2.20 GB
tinyllama-1.1b-intermediate-step-1431k-3t.q2_k.gguf	q2_k	483.12 MB
tinyllama-1.1b-intermediate-step-1431k-3t.q3_k_m.gguf	q3_k_m	550.82 MB
tinyllama-1.1b-intermediate-step-1431k-3t.q4_k_m.gguf	q4_k_m	668.79 MB
tinyllama-1.1b-intermediate-step-1431k-3t.q5_k_m.gguf	q5_k_m	783.02 MB
tinyllama-1.1b-intermediate-step-1431k-3t.q6_k.gguf	q6_k	904.39 MB
tinyllama-1.1b-intermediate-step-1431k-3t.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

TinyLlama-1.1B

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 Collection

This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.

Eval

Model	Pretrain Tokens	HellaSwag	Obqa	WinoGrande	ARC_c	ARC_e	boolq	piqa	avg
Pythia-1.0B	300B	47.16	31.40	53.43	27.05	48.99	60.83	69.21	48.30
TinyLlama-1.1B-intermediate-step-50K-104b	103B	43.50	29.80	53.28	24.32	44.91	59.66	67.30	46.11
TinyLlama-1.1B-intermediate-step-240k-503b	503B	49.56	31.40	55.80	26.54	48.32	56.91	69.42	48.28
TinyLlama-1.1B-intermediate-step-480k-1007B	1007B	52.54	33.40	55.96	27.82	52.36	59.54	69.91	50.22
TinyLlama-1.1B-intermediate-step-715k-1.5T	1.5T	53.68	35.20	58.33	29.18	51.89	59.08	71.65	51.29
TinyLlama-1.1B-intermediate-step-955k-2T	2T	54.63	33.40	56.83	28.07	54.67	63.21	70.67	51.64
TinyLlama-1.1B-intermediate-step-1195k-token-2.5T	2.5T	58.96	34.40	58.72	31.91	56.78	63.21	73.07	53.86

Downloads last month: 77

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

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5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF

Base model

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Quantized

(14)

this model

afrideva
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TinyLlama-1.1B-intermediate-step-1431k-3T-GGUF