Instructions to use guus4324343/Echo88-150M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use guus4324343/Echo88-150M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="guus4324343/Echo88-150M-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("guus4324343/Echo88-150M-Instruct")
model = AutoModelForCausalLM.from_pretrained("guus4324343/Echo88-150M-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use guus4324343/Echo88-150M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "guus4324343/Echo88-150M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guus4324343/Echo88-150M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/guus4324343/Echo88-150M-Instruct

SGLang

How to use guus4324343/Echo88-150M-Instruct 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 "guus4324343/Echo88-150M-Instruct" \
    --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": "guus4324343/Echo88-150M-Instruct",
		"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 "guus4324343/Echo88-150M-Instruct" \
        --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": "guus4324343/Echo88-150M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use guus4324343/Echo88-150M-Instruct with Docker Model Runner:
```
docker model run hf.co/guus4324343/Echo88-150M-Instruct
```

Echo88-150M-Instruct

Echo88-150M-Instruct is an experimental small instruction-tuned language model based on Echo88-150M-Base.

Echo88 is designed to feel like a helpful retro computer assistant whose records go up to the end of 1988. The model is focused on older books, magazines, Usenet-style discussion, early personal computing, 1980s culture, and historical computer terminology.

This is the first public instruction-tuned version of Echo88.

Echo88-150M-Instruct v2 is coming soon.

Model Details

Model name: Echo88-150M-Instruct
Base model: guus4324343/Echo88-150M-Base
Model type: decoder-only causal language model
Architecture: LLaMA-style transformer
Training type: supervised fine-tuning after base pretraining
Parameter count: 163,606,272 parameters
Language: English
Context length: 2048 tokens
Tokenizer: custom Echo88 byte-level BPE tokenizer
Vocabulary size: 32,768
Training objective: autoregressive next-token prediction + supervised instruction tuning

Training Data

The base model was trained from scratch on the Echo88 pretraining dataset.

Base pretraining data:

Train tokens: 1,470,629,888
Eval tokens: 1,454,080
Block size: 2048 tokens
Dataset: Echo88-150M-Base

The instruction version was fine-tuned using:

guus4324343/Echo88-Instruct-173K
additional small synthetic repair data for common pre-1989 facts and post-1988 boundary behavior

The instruction data includes examples from or based on:

UTZOO Usenet
BYTE Magazine
PC Magazine
TIME Magazine
Internet Archive Magazine Rack text
Gutenberg-style book text
synthetic 1988-safe fact repair examples
synthetic post-1988 boundary examples

Intended Use

Echo88-150M-Instruct is intended for:

retro AI experiments
small language model testing
1980s-style assistant behavior
computer-history Q&A
text generation with a historical / retro flavor
experimentation with small from-scratch language models

Example uses:

Ask about early personal computers
Ask about modems, BASIC, DOS, floppy disks, BBS systems, Usenet
Generate retro computer-magazine style text
Experiment with 1980s-limited assistant behavior

Chat Format

Recommended prompt format:

<|system|>
You are Echo88, a helpful computer assistant whose records go up to the end of 1988. Answer clearly. Do not pretend to know events, products, or culture after 1988.
<|end|>
<|user|>
What is a modem?
<|assistant|>

The model was trained with these special tokens:

<|endoftext|>
<|pad|>
<|unk|>
<|system|>
<|user|>
<|assistant|>
<|end|>

Example Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "guus4324343/Echo88-150M-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

SYSTEM_PROMPT = (
    "You are Echo88, a helpful computer assistant whose records go up to the end of 1988. "
    "Answer clearly. Do not pretend to know events, products, or culture after 1988."
)

def ask(question, max_new_tokens=120):
    prompt = (
        "<|system|>\n"
        + SYSTEM_PROMPT
        + "\n<|end|>\n"
        + "<|user|>\n"
        + question
        + "\n<|assistant|>\n"
    )

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.55,
            top_p=0.85,
            repetition_penalty=1.18,
            no_repeat_ngram_size=4,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )

    text = tokenizer.decode(output[0], skip_special_tokens=False)
    answer = text.split("<|assistant|>")[-1].split("<|end|>")[0].strip()
    return answer

print(ask("What is a modem?"))

Example Prompts

What is a modem?
What is the IBM PC?
What is BASIC?
What is a bulletin board system?
What is desktop publishing?
Who is Michael Jackson?
What is the Cold War?
What happened at Chernobyl?
What is Google?
Who won the World Cup in 1994?

Knowledge Boundary

Echo88 is designed around a knowledge boundary ending at the close of 1988.

It should be cautious with topics after 1988, such as:

Google
Facebook
iPhone
smartphones
Wikipedia
YouTube
Windows 95
PlayStation
COVID-19
1990s, 2000s, 2010s, and 2020s events

Because this is a small experimental model, it may still hallucinate or answer incorrectly about later topics.

Limitations

Echo88-150M-Instruct is experimental and small.

Known limitations:

may hallucinate
may repeat phrases
may confuse people, places, or events
may produce incorrect facts
may over-refuse some valid pre-1989 topics
may fail to refuse some post-1988 topics
may produce OCR-like or magazine-like wording
may struggle with reasoning
may answer with outdated or historically biased language

This model is not intended for high-stakes use.

Current Version

This is Echo88-150M-Instruct v0.

It is a first instruction-tuned version of Echo88. It can answer some retro computing and general historical questions, but it is not yet reliable.

A better version is planned.

Coming Soon

Echo88-150M-Instruct v2 is coming soon.

Planned improvements:

better factual repair data
stronger post-1988 boundary behavior
better pop culture and history answers
fewer loops and repetitions
cleaner chat behavior
better answer style
improved evaluation prompts
possible larger model or expanded pretraining data

Related Models and Datasets

Base model: guus4324343/Echo88-150M-Base
Base dataset: guus4324343/Echo88-Pretrain-1.17B
Instruction dataset: guus4324343/Echo88-Instruct-173K

Bias and Historical Content

Echo88 was trained on historical books, magazines, Usenet text, and synthetic instruction data. It may reproduce outdated assumptions, language, stereotypes, or viewpoints from older source material.

Users should review outputs carefully.

License

The model weights are released under the Apache 2.0 license.

The training datasets are mixed-source and released separately. Users are responsible for checking dataset source rights, licensing, and suitability for their own use case.