Instructions to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mifeng09/my_final_llama_model_v2_add_wiki_fix_resume")
model = PeftModel.from_pretrained(base_model, "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate")

Transformers

How to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate with Transformers:

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

pipe = pipeline("text-generation", model="mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate

SGLang

How to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate 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 "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate" \
    --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": "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate",
		"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 "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate" \
        --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": "mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate with Docker Model Runner:
```
docker model run hf.co/mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate
```

llama_lora_20k_alpaca_sencond_tiny_learingRate

This model is a fine-tuned version of mifeng09/my_final_llama_model_v2_add_wiki_fix_resume on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.8042

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: 8e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
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: 0.1
lr_scheduler_warmup_steps: 67
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.8201	0.2227	50	4.0046
4.0256	0.4454	100	3.9221
3.6268	0.6682	150	3.8848
3.8767	0.8909	200	3.8590
3.8853	1.1114	250	3.8412
3.8609	1.3341	300	3.8283
3.8614	1.5568	350	3.8205
3.8693	1.7795	400	3.8144
3.7834	2.0	450	3.8096
3.9981	2.2227	500	3.8062
3.7208	2.4454	550	3.8050
3.9458	2.6682	600	3.8044
3.7216	2.8909	650	3.8042

Framework versions

PEFT 0.18.0
Transformers 4.57.3
Pytorch 2.9.0+cpu
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: -

Model tree for mifeng09/llama_lora_20k_alpaca_sencond_tiny_learingRate

Base model

mifeng09/my_final_llama_model_v2_add_wiki_fix_resume

Adapter

(2)

this model