Piaget-0.6B

Name: Piaget-0.6B
Author: gustavecortal

307

600M

1 language

license:mit

gustavecortal

Language Model

OTHER

0.6B params

New

307 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

Piaget, a language model finetuned on 15k psychological and philosophical reasoning traces.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

How to usepythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.pipelines import pipeline
import torch

repo = "gustavecortal/Piaget-0.6B"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template(
    [
        {
            "role": "user",
            "content": "Create a new psychotherapeutic technique based on cybernetic principles",
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

print(pipe(prompt, max_new_tokens=2048, do_sample=True)[0]["generated_text"])

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.