Instructions to use yam-peleg/Experiment29-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yam-peleg/Experiment29-7B with Transformers:

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

pipe = pipeline("text-generation", model="yam-peleg/Experiment29-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Experiment29-7B")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Experiment29-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yam-peleg/Experiment29-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yam-peleg/Experiment29-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yam-peleg/Experiment29-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yam-peleg/Experiment29-7B

SGLang

How to use yam-peleg/Experiment29-7B 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 "yam-peleg/Experiment29-7B" \
    --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": "yam-peleg/Experiment29-7B",
		"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 "yam-peleg/Experiment29-7B" \
        --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": "yam-peleg/Experiment29-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yam-peleg/Experiment29-7B with Docker Model Runner:
```
docker model run hf.co/yam-peleg/Experiment29-7B
```

Experiment29-7B

An experiment for testing and refining a specific training and evaluation pipeline research framework.

This experiment aims to identify potential optimizations, focusing on data engineering, architecture efficiency, and evaluation performance.

The goal is to evaluate the effectiveness of a new training / evaluation pipeline for LLMs.

The experiment will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement.

More details in the future experiments.

license: apache-2.0

Downloads last month: 77

Safetensors

Model size

7B params

Tensor type

F16

Model tree for yam-peleg/Experiment29-7B

Finetunes

1 model

Merges

4 models

yam-peleg
/

Experiment29-7B

license: apache-2.0

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