Instructions to use Pection/llama3-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pection/llama3-finetune with Transformers:

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

pipe = pipeline("text-generation", model="Pection/llama3-finetune")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pection/llama3-finetune")
model = AutoModelForCausalLM.from_pretrained("Pection/llama3-finetune")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pection/llama3-finetune with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Pection/llama3-finetune

SGLang

How to use Pection/llama3-finetune 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 "Pection/llama3-finetune" \
    --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": "Pection/llama3-finetune",
		"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 "Pection/llama3-finetune" \
        --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": "Pection/llama3-finetune",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pection/llama3-finetune with Docker Model Runner:
```
docker model run hf.co/Pection/llama3-finetune
```

Pection commited on Nov 27, 2024

Commit

bf315e2

verified ·

1 Parent(s): ac5be09

Update README.md

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README.md +10 -2

README.md CHANGED Viewed

@@ -5,13 +5,21 @@ language:
 library_name: transformers
 base_model:
 - meta-llama/Llama-3.2-1B
-widget:
-- text: Once upon a time
 tags:
   - text-generation
 pipeline_tag: text-generation
 ---
 ---
 # LLaMA 3 Fine-Tuned Model

 library_name: transformers
 base_model:
 - meta-llama/Llama-3.2-1B
 tags:
   - text-generation
 pipeline_tag: text-generation
+inference:
+  parameters:
+    temperature: 0.5
+widget:
+- messages:
+  - role: user
+    content: What is your favorite condiment?
 ---
+extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+---
 ---
 # LLaMA 3 Fine-Tuned Model