Instructions to use afrizalha/Kancil-V1-llama3-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrizalha/Kancil-V1-llama3-4bit with Transformers:

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

pipe = pipeline("text-generation", model="afrizalha/Kancil-V1-llama3-4bit")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("afrizalha/Kancil-V1-llama3-4bit")
model = AutoModelForCausalLM.from_pretrained("afrizalha/Kancil-V1-llama3-4bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use afrizalha/Kancil-V1-llama3-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrizalha/Kancil-V1-llama3-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrizalha/Kancil-V1-llama3-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrizalha/Kancil-V1-llama3-4bit

SGLang

How to use afrizalha/Kancil-V1-llama3-4bit 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 "afrizalha/Kancil-V1-llama3-4bit" \
    --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": "afrizalha/Kancil-V1-llama3-4bit",
		"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 "afrizalha/Kancil-V1-llama3-4bit" \
        --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": "afrizalha/Kancil-V1-llama3-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use afrizalha/Kancil-V1-llama3-4bit with Docker Model Runner:
```
docker model run hf.co/afrizalha/Kancil-V1-llama3-4bit
```

Catinthebag commited on May 31, 2024

Commit

bbb4952

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1 Parent(s): 161de81

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@@ -35,12 +35,11 @@ inference: false
     <img src="https://imgur.com/9nG5J1T.png" alt="Kancil" width="600" height="300">
     <p><em>Kancil is a fine-tuned version of Llama 3 8B using synthetic QA dataset generated with Llama 3 70B. Version zero of Kancil is the first generative Indonesian LLM gain functional instruction performance using solely synthetic data.</em></p>
     <p><strong><a href="https://colab.research.google.com/drive/1526QJYfk32X1CqYKX7IA_FFcIHLXbOkx?usp=sharing" style="color: blue; font-family: Tahoma;">❕Go straight to the colab demo❕</a></strong></p>
-    <p><em style="color: seagreen;">Compatibility erorrs are fixed! The colab should work fine now.</em></p>
 </center>
 Selamat datang!
-I am ultra-overjoyed to introduce you... the 🦌 Kancil! It's a fine-tuned version of Llama 3 8B with the Tumpeng, an instruction dataset of 6.7 million words. Both the model and dataset is openly available in Huggingface.
 📚 The dataset was synthetically generated from Llama 3 70B. A big problem with existing Indonesian instruction dataset is they're in reality not-very-good-translations of English datasets. Llama 3 70B can generate fluent Indonesian! (with minor caveats 😔)

     <img src="https://imgur.com/9nG5J1T.png" alt="Kancil" width="600" height="300">
     <p><em>Kancil is a fine-tuned version of Llama 3 8B using synthetic QA dataset generated with Llama 3 70B. Version zero of Kancil is the first generative Indonesian LLM gain functional instruction performance using solely synthetic data.</em></p>
     <p><strong><a href="https://colab.research.google.com/drive/1526QJYfk32X1CqYKX7IA_FFcIHLXbOkx?usp=sharing" style="color: blue; font-family: Tahoma;">❕Go straight to the colab demo❕</a></strong></p>
 </center>
 Selamat datang!
+I am ultra-overjoyed to introduce you... the 🦌 Kancil! It's a fine-tuned version of Llama 3 8B with the Tumpeng, an instruction dataset of 14.8 million words. Both the model and dataset is openly available in Huggingface.
 📚 The dataset was synthetically generated from Llama 3 70B. A big problem with existing Indonesian instruction dataset is they're in reality not-very-good-translations of English datasets. Llama 3 70B can generate fluent Indonesian! (with minor caveats 😔)