Instructions to use Ehraim/SequentialLearnerv13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ehraim/SequentialLearnerv13 with Transformers:

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

pipe = pipeline("text-generation", model="Ehraim/SequentialLearnerv13")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ehraim/SequentialLearnerv13")
model = AutoModelForCausalLM.from_pretrained("Ehraim/SequentialLearnerv13")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Ehraim/SequentialLearnerv13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ehraim/SequentialLearnerv13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ehraim/SequentialLearnerv13",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ehraim/SequentialLearnerv13

SGLang

How to use Ehraim/SequentialLearnerv13 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 "Ehraim/SequentialLearnerv13" \
    --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": "Ehraim/SequentialLearnerv13",
		"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 "Ehraim/SequentialLearnerv13" \
        --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": "Ehraim/SequentialLearnerv13",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ehraim/SequentialLearnerv13 with Docker Model Runner:
```
docker model run hf.co/Ehraim/SequentialLearnerv13
```

SequentialLearnerv13 / README.md

Ehraim

End of training

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metadata

license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
  - generated_from_trainer
model-index:
  - name: SequentialLearnerv13
    results: []

SequentialLearnerv13

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6029

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.5
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	165	0.5458
No log	2.0	331	0.5264
No log	3.0	497	0.5314
0.5201	3.98	660	0.6029

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0