Instructions to use postbot/pythia-160m-hq-emails with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use postbot/pythia-160m-hq-emails with Transformers:

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

pipe = pipeline("text-generation", model="postbot/pythia-160m-hq-emails")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("postbot/pythia-160m-hq-emails")
model = AutoModelForCausalLM.from_pretrained("postbot/pythia-160m-hq-emails", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use postbot/pythia-160m-hq-emails with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "postbot/pythia-160m-hq-emails"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "postbot/pythia-160m-hq-emails",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/postbot/pythia-160m-hq-emails

SGLang

How to use postbot/pythia-160m-hq-emails 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 "postbot/pythia-160m-hq-emails" \
    --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": "postbot/pythia-160m-hq-emails",
		"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 "postbot/pythia-160m-hq-emails" \
        --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": "postbot/pythia-160m-hq-emails",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use postbot/pythia-160m-hq-emails with Docker Model Runner:
```
docker model run hf.co/postbot/pythia-160m-hq-emails
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

pythia-160m-hq-emails-v4

This model is a fine-tuned version of EleutherAI/pythia-160m-deduped on the postbot/multi-emails-hq dataset. It achieves the following results on the evaluation set:

Loss: 2.2856
Accuracy: 0.6113
perplexity: 9.8313

Model description

this is v4

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: 0.0006
train_batch_size: 4
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 4.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.412	0.99	76	2.5027	0.5458
1.9702	1.99	152	2.2757	0.5850
1.4628	2.99	228	2.2162	0.6082
1.1662	3.99	304	2.2856	0.6113

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.0
Tokenizers 0.13.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	25.12
ARC (25-shot)	23.12
HellaSwag (10-shot)	30.05
MMLU (5-shot)	26.58
TruthfulQA (0-shot)	45.51
Winogrande (5-shot)	50.28
GSM8K (5-shot)	0.0
DROP (3-shot)	0.31

Downloads last month: 15

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for postbot/pythia-160m-hq-emails

Base model

EleutherAI/pythia-160m-deduped

Finetuned

(52)

this model

Quantizations

2 models

Spaces using postbot/pythia-160m-hq-emails 5

Evaluation results

Accuracy on postbot/multi-emails-hq
self-reported

0.611