Instructions to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Salesforce/Llama-xLAM-2-8b-fc-r")
model = PeftModel.from_pretrained(base_model, "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64")

Transformers

How to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 with Transformers:

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

pipe = pipeline("text-generation", model="a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64

SGLang

How to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 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 "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64" \
    --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": "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64",
		"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 "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64" \
        --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": "a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64 with Docker Model Runner:
```
docker model run hf.co/a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64
```

xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64

This model is a fine-tuned version of Salesforce/Llama-xLAM-2-8b-fc-r on the multi_turn_miss_func_zh_tw_function_mix_sharegpt, the multi_turn_miss_param_zh_tw_function_mix_sharegpt and the multi_turn_zh_tw_function_mix_sharegpt datasets. It achieves the following results on the evaluation set:

Loss: 1.8052

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
2.3656	0.3108	450	2.2669
1.4947	0.6217	900	1.8678
1.56	0.9325	1350	1.8056

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.4.1+cu121
Datasets 4.0.0
Tokenizers 0.21.4

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Model tree for a3ilab-llm-uncertainty/xlam_8B_pythonic_zhtw_lr5e-7_ep1_16_64

Base model

Salesforce/Llama-xLAM-2-8b-fc-r

Adapter

(98)

this model