Instructions to use CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 with Transformers:

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

pipe = pipeline("text-generation", model="CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16")
model = AutoModelForCausalLM.from_pretrained("CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16

SGLang

How to use CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 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 "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16" \
    --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": "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16",
		"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 "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16" \
        --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": "CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 with Docker Model Runner:
```
docker model run hf.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
```

Model Card for Model ID

在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w

Fine-Tuning Information

GPU: RTX4090 (single core / 24564MiB)
model: meta-llama/Llama-2-13b-hf
dataset: huangyt/FINETUNE1 (共約17w筆訓練集)
peft_type: LoRA
lora_rank: 16
lora_target: gate_proj, up_proj, down_proj
per_device_train_batch_size: 8
gradient_accumulation_steps: 8
learning_rate : 5e-5
epoch: 1
precision: bf16
quantization: load_in_4bit

Fine-Tuning Detail

train_loss: 0.66
train_runtime: 16:26:58 (use deepspeed)

Evaluation

評估結果來自HuggingFaceH4/open_llm_leaderboard
與Llama-2-13b比較4種Benchmark，包含ARC、HellaSwag、MMLU、TruthfulQA

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA
meta-llama/Llama-2-13b-hf	56.9	58.11	80.97	54.34	34.17
meta-llama/Llama-2-13b-chat-hf	59.93	59.04	81.94	54.64	44.12
CHIH-HUNG/llama-2-13b-Fintune_1_17w	58.24	59.47	81	54.31	38.17
CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj	58.49	59.73	81.06	54.53	38.64
CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj	58.81	57.17	82.26	55.89	39.93
CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16	58.86	57.25	82.27	56.16	39.75
CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4	58.71	56.74	82.27	56.18	39.65

How to convert dataset to json

在load_dataset中輸入資料集名稱，並且在take中輸入要取前幾筆資料
觀察該資料集的欄位名稱，填入example欄位中(例如system_prompt、question、response)
最後指定json檔儲存位置 (json_filename)

import json
from datasets import load_dataset

# 讀取數據集，take可以取得該數據集前n筆資料
dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True)

# 提取所需欄位並建立新的字典列表
extracted_data = []
for example in dataset:
    extracted_example = {
        "instruction": example["instruction"],
        "input": example["input"],
        "output": example["output"]
    }
    extracted_data.append(extracted_example)

# 指定 JSON 文件名稱
json_filename = "huangyt_FINETUNE_1.json"

# 寫入 JSON 文件
with open(json_filename, "w") as json_file:
    json.dump(extracted_data, json_file, indent=4)

print(f"數據已提取並保存為 {json_filename}")

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CHIH-HUNG
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llama-2-13b-FINETUNE1_17w-r16

Model Card for Model ID

Fine-Tuning Information

Fine-Tuning Detail

Evaluation

How to convert dataset to json

Dataset used to train CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16