Instructions to use RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits

SGLang

How to use RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits 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 "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits" \
    --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": "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits",
		"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 "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits" \
        --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": "RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Yash21_-_TinyYi-7B-Test-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

TinyYi-7B-Test - bnb 8bits

Model creator: https://huggingface.co/Yash21/
Original model: https://huggingface.co/Yash21/TinyYi-7B-Test/

Original model description:

license: apache-2.0 tags: - merge - mergekit

TinyYi-7B-Test

This model is a merge of the following models:

🧩 Configuration

slices:
  - sources:
      - model: Yash21/DeepYi-Base
        layer_range: [0, 32]
      - model: Yash21/DeepYi-Second
        layer_range: [0, 32]
merge_method: slerp
base_model: Yash21/DeepYi-Base
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Please Reach Out to maratheyash108@gmail.com if you want to support me to Fine tune the models and create more such exciting models

Downloads last month: 5

Safetensors

Model size

6B params

Tensor type

F32

F16