Instructions to use fluently-lm/Llama-TI-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fluently-lm/Llama-TI-8B with Transformers:

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

pipe = pipeline("text-generation", model="fluently-lm/Llama-TI-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fluently-lm/Llama-TI-8B")
model = AutoModelForCausalLM.from_pretrained("fluently-lm/Llama-TI-8B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fluently-lm/Llama-TI-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fluently-lm/Llama-TI-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fluently-lm/Llama-TI-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fluently-lm/Llama-TI-8B

SGLang

How to use fluently-lm/Llama-TI-8B 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 "fluently-lm/Llama-TI-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fluently-lm/Llama-TI-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "fluently-lm/Llama-TI-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fluently-lm/Llama-TI-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fluently-lm/Llama-TI-8B with Docker Model Runner:
```
docker model run hf.co/fluently-lm/Llama-TI-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3.1 8B TI

Llama TI is an improved Llama (from Meta AI), some aspects of the model have been revised and some features have been added.

Info

Main

The model is based on Meta-Llama-3.1-8B, and has the same 8.03B parameters. The Llama3 architecture (LlamaForCausalLM) has been preserved and the model launch methods are the same.

Differences

Thanks to additional training and advanced merging, it was possible to improve mathematical, biological, reasoning and writing skills.

Now the model can:

Count well and solve mathematical/physical problems
Reason/think logically
Write creatively (in many languages)
Code well
Process/analyze large texts

Where is the chat version (instruct)?

It is available here!

Special thanks to:

Meta AI, NVIDIA, Arcee AI, SkyWork, NousReaserch, Unsloth and Project Fluently.

Developed and uploaded by ehristoforu.

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for fluently-lm/Llama-TI-8B

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1790)

this model

Quantizations

2 models