Instructions to use Dimeltech/Tenezis-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dimeltech/Tenezis-8B with Transformers:

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

pipe = pipeline("text-generation", model="Dimeltech/Tenezis-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dimeltech/Tenezis-8B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dimeltech/Tenezis-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dimeltech/Tenezis-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dimeltech/Tenezis-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dimeltech/Tenezis-8B

SGLang

How to use Dimeltech/Tenezis-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 "Dimeltech/Tenezis-8B" \
    --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": "Dimeltech/Tenezis-8B",
		"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 "Dimeltech/Tenezis-8B" \
        --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": "Dimeltech/Tenezis-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dimeltech/Tenezis-8B 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 Dimeltech/Tenezis-8B 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 Dimeltech/Tenezis-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dimeltech/Tenezis-8B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dimeltech/Tenezis-8B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dimeltech/Tenezis-8B with Docker Model Runner:
```
docker model run hf.co/Dimeltech/Tenezis-8B
```

Therence-NG commited on Jul 30, 2024

Commit

fc0c6f2

verified ·

1 Parent(s): ce80cba

updates

Browse files

Files changed (1) hide show

README.md +31 -22

README.md CHANGED Viewed

@@ -1,22 +1,31 @@
----
-base_model: unsloth/llama-3-8b-bnb-4bit
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
----
-# Uploaded  model
-- **Developed by:** Dimeltech
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

+# Tenezis-8b: Multilingual Model for Africa
+## Overview
+Tenezis-8b is an artificial intelligence model developed by Dimeltech, specifically designed to include and value African languages. This model is based on `unsloth/llama-3-8b-bnb-4bit` and has been fine-tuned for fast and efficient text generation.
+## Features
+- **Base Model:** `unsloth/llama-3-8b-bnb-4bit`
+- **Number of Parameters:** 8 billion
+- **License:** Apache 2.0
+- **Supported Languages:** English, Zulu, Wolof, Swahili, Hausa, Amharic, Yoruba, Xhosa, Shona, and Arabic.
+- **Technologies Used:**
+  - [Unsloth](https://github.com/unslothai/unsloth) for rapid training optimization
+  - [Transformers](https://huggingface.co/transformers/) by Hugging Face for language model management
+  - [TRL (Transformers Reinforcement Learning)](https://github.com/huggingface/trl) for reinforcement fine-tuning
+## Developed by Dimeltech
+Dimeltech is a company dedicated to technological innovation and digital inclusion, with a particular focus on Africa. Our mission is to make artificial intelligence technologies accessible and beneficial to everyone.
+## License
+This model is distributed under the Apache 2.0 license, allowing for flexible and open use while ensuring transparency and collaborative development.
+## Contribution and Support
+We encourage community contributions to enhance and expand the capabilities of Tenezis-8b. For more information on contributing, please refer to our [contribution guide](https://github.com/dimeltech/tenezis-8b/contributing).
+For any support or questions, please contact our team via our [GitHub page](https://github.com/dimeltech/tenezis-8b) or through our [community forum](https://community.dimeltech.com).