Instructions to use teilomillet/MiniMerlin-3b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teilomillet/MiniMerlin-3b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="teilomillet/MiniMerlin-3b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teilomillet/MiniMerlin-3b-v0.1")
model = AutoModelForCausalLM.from_pretrained("teilomillet/MiniMerlin-3b-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use teilomillet/MiniMerlin-3b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teilomillet/MiniMerlin-3b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teilomillet/MiniMerlin-3b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teilomillet/MiniMerlin-3b-v0.1

SGLang

How to use teilomillet/MiniMerlin-3b-v0.1 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 "teilomillet/MiniMerlin-3b-v0.1" \
    --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": "teilomillet/MiniMerlin-3b-v0.1",
		"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 "teilomillet/MiniMerlin-3b-v0.1" \
        --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": "teilomillet/MiniMerlin-3b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teilomillet/MiniMerlin-3b-v0.1 with Docker Model Runner:
```
docker model run hf.co/teilomillet/MiniMerlin-3b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model : https://huggingface.co/GeneZC/MiniChat-1.5-3B
FT : @teilomillet
Instruction tune using QLoRA on a french dataset for 1 epoch. The aim was to test and try the dataset. Implementing a customization via a dataset and fine-tuning on it. The way to respond is also important to see if it's taken from the dataset and add to the customization.

This is the first of a long serie of multiple models. Aimed to be minuscule as possible.

Batch : 6
Gradient step : 1
Epoch : 1
Lr : 0.0002

Downloads last month: 125

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for teilomillet/MiniMerlin-3b-v0.1

Quantizations

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Collection including teilomillet/MiniMerlin-3b-v0.1

Merlin

Collection

4 items • Updated Jan 6, 2024