Instructions to use mifeng09/my_final_llama_model_v2_add_wiki_fix_resume with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mifeng09/my_final_llama_model_v2_add_wiki_fix_resume with Transformers:

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

pipe = pipeline("text-generation", model="mifeng09/my_final_llama_model_v2_add_wiki_fix_resume")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mifeng09/my_final_llama_model_v2_add_wiki_fix_resume")
model = AutoModelForCausalLM.from_pretrained("mifeng09/my_final_llama_model_v2_add_wiki_fix_resume", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mifeng09/my_final_llama_model_v2_add_wiki_fix_resume with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mifeng09/my_final_llama_model_v2_add_wiki_fix_resume"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mifeng09/my_final_llama_model_v2_add_wiki_fix_resume",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mifeng09/my_final_llama_model_v2_add_wiki_fix_resume

SGLang

How to use mifeng09/my_final_llama_model_v2_add_wiki_fix_resume 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/my_final_llama_model_v2_add_wiki_fix_resume" \
    --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/my_final_llama_model_v2_add_wiki_fix_resume",
		"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/my_final_llama_model_v2_add_wiki_fix_resume" \
        --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/my_final_llama_model_v2_add_wiki_fix_resume",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mifeng09/my_final_llama_model_v2_add_wiki_fix_resume with Docker Model Runner:
```
docker model run hf.co/mifeng09/my_final_llama_model_v2_add_wiki_fix_resume
```

my_final_llama_model_v2_add_wiki_fix_resume

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.0000

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: 0.0001
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
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: linear
training_steps: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.2118	0.1537	500	4.1942
4.1614	0.3074	1000	4.1656
4.1253	0.4611	1500	4.1383
4.1131	0.6148	2000	4.1153
4.0994	0.7685	2500	4.0954
4.0668	0.9222	3000	4.0779
4.0383	1.0756	3500	4.0644
4.049	1.2293	4000	4.0529
4.0126	1.3830	4500	4.0404
3.9655	1.5367	5000	4.0297
3.9358	1.6904	5500	4.0210
3.9137	1.8441	6000	4.0134
4.0083	1.9978	6500	4.0067
3.8427	2.1512	7000	4.0045
3.8823	2.3049	7500	4.0014
3.9662	2.4586	8000	4.0000

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.4.1
Tokenizers 0.22.1

Downloads last month: 8

Safetensors

Model size

64.4M params

Tensor type

F32

Model tree for mifeng09/my_final_llama_model_v2_add_wiki_fix_resume

Adapters

2 models