Instructions to use teilomillet/Potato-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teilomillet/Potato-3B with Transformers:

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

pipe = pipeline("text-generation", model="teilomillet/Potato-3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teilomillet/Potato-3B")
model = AutoModelForCausalLM.from_pretrained("teilomillet/Potato-3B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teilomillet/Potato-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/teilomillet/Potato-3B

SGLang

How to use teilomillet/Potato-3B 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/Potato-3B" \
    --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/Potato-3B",
		"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/Potato-3B" \
        --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/Potato-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Merging stuff to make a potato. Idk about it, might delete later.

Merge of MiniMerlin via Task arithmetic using mergekit. There was no goal except merging. Interest in the outcome tho. I might need to fine-tune it more.

FT on more french data (Merlin).

Je pense qu'il s'agit du meilleur model français en 3B. Essayez le.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

model = AutoModelForCausalLM.from_pretrained(
    "teilomillet/Potato-3B",
    revision="0.1",
    return_dict=True,
    torch_dtype=torch.bfloat16,
    device_map='auto'
)

tokenizer = AutoTokenizer.from_pretrained("teilomillet/Potato-3B")
tokenizer.pad_token = tokenizer.eos_token

text = "[|User|] Comment faire un bon plat ? </s>[|Assistant|]"
inputs = tokenizer(text, return_tensors="pt").to(0)

outputs = model.generate(**inputs, max_new_tokens=800)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

#merge

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for teilomillet/Potato-3B

Adapters

1 model

Quantizations

1 model

Collection including teilomillet/Potato-3B

Potato

Collection

2 items • Updated Jan 6, 2024