Instructions to use BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit with Transformers:

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

pipe = pipeline("text-generation", model="BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit")
model = AutoModelForCausalLM.from_pretrained("BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit

SGLang

How to use BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit 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 "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit" \
    --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": "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit",
		"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 "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit" \
        --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": "BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit with Docker Model Runner:
```
docker model run hf.co/BrokenSoul/Meta-Llama-3.1-8B-function-calling-16bit
```

BrokenSoul commited on Jul 30, 2024

Commit

f2b386d

verified ·

1 Parent(s): e41bd72

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -17

README.md CHANGED Viewed

@@ -1,19 +1,19 @@
----
-base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
-- sft
-datasets:
-- Salesforce/xlam-function-calling-60k
-library_name: transformers
----
 # Uploaded  model
@@ -21,6 +21,6 @@ library_name: transformers
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library, It is a fine-tuning about function calling using the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

+---
+base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
+datasets:
+- Salesforce/xlam-function-calling-60k
+library_name: transformers
+---
 # Uploaded  model
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library, It is a fine-tuning about function calling using the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)