Instructions to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v0.6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v0.6")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v0.6", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 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 TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
# Run inference directly in the terminal:
llama cli -hf TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
# Run inference directly in the terminal:
llama cli -hf TinyLlama/TinyLlama-1.1B-Chat-v0.6: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 TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf TinyLlama/TinyLlama-1.1B-Chat-v0.6: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 TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0

Use Docker

docker model run hf.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0

LM Studio
Jan

vLLM

How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TinyLlama/TinyLlama-1.1B-Chat-v0.6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0

SGLang

How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TinyLlama/TinyLlama-1.1B-Chat-v0.6" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TinyLlama/TinyLlama-1.1B-Chat-v0.6" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with Ollama:
```
ollama run hf.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
```
Unsloth Desktop
Docker Model Runner
How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with Docker Model Runner:
```
docker model run hf.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0
```

Lemonade

How to use TinyLlama/TinyLlama-1.1B-Chat-v0.6 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TinyLlama/TinyLlama-1.1B-Chat-v0.6:Q4_0

Run and chat with the model

lemonade run user.TinyLlama-1.1B-Chat-v0.6-Q4_0

List all available models

lemonade list

Atomic Chat

Anonymouspro commited on Jul 18, 2024

Commit

4987c7b

verified ·

1 Parent(s): bf9ae1c

Update README.md

Browse files

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README.md +2 -2

README.md CHANGED Viewed

@@ -9,7 +9,7 @@ language:
 ---
 <div align="center">
-# TinyLlama-1.1B
 </div>
 https://github.com/jzhang38/TinyLlama
@@ -19,7 +19,7 @@ The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion to
 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-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.
 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."

 ---
 <div align="center">
+# TinyLlama-1.1B by Anonymous Pro BD cyber 71
 </div>
 https://github.com/jzhang38/TinyLlama
 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 Anonymous Pro BD Cyber 71
 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.
 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."