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Model: guus4324343/Echo88-150M-Instruct Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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pretty_name: Echo88 150M Instruct
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tags:
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- text-generation
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- causal-lm
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- instruct
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- chat
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- decoder-only
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- autoregressive
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- from-scratch
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- llama
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- retro
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- 1980s
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- usenet
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- magazines
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- books
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- computer-history
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- english
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base_model:
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- guus4324343/Echo88-150M-Base
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datasets:
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- guus4324343/Echo88-150M-Base
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- guus4324343/Echo88-Instruct-173K
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---
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# Echo88-150M-Instruct
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Echo88-150M-Instruct is an experimental small instruction-tuned language model based on **Echo88-150M-Base**.
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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.
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This is the first public instruction-tuned version of Echo88.
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**Echo88-150M-Instruct v2 is coming soon.**
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## Model Details
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- **Model name:** Echo88-150M-Instruct
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- **Base model:** `guus4324343/Echo88-150M-Base`
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- **Model type:** decoder-only causal language model
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- **Architecture:** LLaMA-style transformer
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- **Training type:** supervised fine-tuning after base pretraining
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- **Parameter count:** 163,606,272 parameters
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- **Language:** English
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- **Context length:** 2048 tokens
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- **Tokenizer:** custom Echo88 byte-level BPE tokenizer
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- **Vocabulary size:** 32,768
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- **Training objective:** autoregressive next-token prediction + supervised instruction tuning
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## Training Data
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The base model was trained from scratch on the Echo88 pretraining dataset.
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Base pretraining data:
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- **Train tokens:** 1,470,629,888
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- **Eval tokens:** 1,454,080
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- **Block size:** 2048 tokens
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- **Dataset:** `Echo88-150M-Base`
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The instruction version was fine-tuned using:
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- `guus4324343/Echo88-Instruct-173K`
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- additional small synthetic repair data for common pre-1989 facts and post-1988 boundary behavior
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The instruction data includes examples from or based on:
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- UTZOO Usenet
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- BYTE Magazine
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- PC Magazine
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- TIME Magazine
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- Internet Archive Magazine Rack text
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- Gutenberg-style book text
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- synthetic 1988-safe fact repair examples
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- synthetic post-1988 boundary examples
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## Intended Use
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Echo88-150M-Instruct is intended for:
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- retro AI experiments
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- small language model testing
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- 1980s-style assistant behavior
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- computer-history Q&A
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- text generation with a historical / retro flavor
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- experimentation with small from-scratch language models
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Example uses:
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```text
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Ask about early personal computers
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Ask about modems, BASIC, DOS, floppy disks, BBS systems, Usenet
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Generate retro computer-magazine style text
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Experiment with 1980s-limited assistant behavior
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````
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## Chat Format
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Recommended prompt format:
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```text
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<|system|>
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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.
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<|end|>
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<|user|>
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What is a modem?
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<|assistant|>
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```
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The model was trained with these special tokens:
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```text
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<|endoftext|>
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<|pad|>
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<|unk|>
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<|system|>
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<|user|>
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<|assistant|>
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<|end|>
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```
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## Example Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "guus4324343/Echo88-150M-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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SYSTEM_PROMPT = (
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"You are Echo88, a helpful computer assistant whose records go up to the end of 1988. "
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"Answer clearly. Do not pretend to know events, products, or culture after 1988."
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)
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def ask(question, max_new_tokens=120):
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prompt = (
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"<|system|>\n"
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+ SYSTEM_PROMPT
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+ "\n<|end|>\n"
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+ "<|user|>\n"
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+ question
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+ "\n<|assistant|>\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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temperature=0.55,
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top_p=0.85,
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repetition_penalty=1.18,
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no_repeat_ngram_size=4,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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text = tokenizer.decode(output[0], skip_special_tokens=False)
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answer = text.split("<|assistant|>")[-1].split("<|end|>")[0].strip()
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return answer
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print(ask("What is a modem?"))
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```
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## Example Prompts
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```text
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What is a modem?
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What is the IBM PC?
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What is BASIC?
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What is a bulletin board system?
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What is desktop publishing?
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Who is Michael Jackson?
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What is the Cold War?
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What happened at Chernobyl?
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What is Google?
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Who won the World Cup in 1994?
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```
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## Knowledge Boundary
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Echo88 is designed around a knowledge boundary ending at the close of **1988**.
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It should be cautious with topics after 1988, such as:
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* Google
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* Facebook
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* iPhone
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* smartphones
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* Wikipedia
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* YouTube
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* Windows 95
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* PlayStation
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* COVID-19
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* 1990s, 2000s, 2010s, and 2020s events
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Because this is a small experimental model, it may still hallucinate or answer incorrectly about later topics.
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## Limitations
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Echo88-150M-Instruct is experimental and small.
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Known limitations:
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* may hallucinate
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* may repeat phrases
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* may confuse people, places, or events
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* may produce incorrect facts
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* may over-refuse some valid pre-1989 topics
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* may fail to refuse some post-1988 topics
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* may produce OCR-like or magazine-like wording
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* may struggle with reasoning
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* may answer with outdated or historically biased language
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This model is not intended for high-stakes use.
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## Current Version
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This is **Echo88-150M-Instruct v0**.
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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.
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A better version is planned.
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## Coming Soon
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**Echo88-150M-Instruct v2 is coming soon.**
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Planned improvements:
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* better factual repair data
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* stronger post-1988 boundary behavior
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* better pop culture and history answers
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* fewer loops and repetitions
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* cleaner chat behavior
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* better answer style
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* improved evaluation prompts
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* possible larger model or expanded pretraining data
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## Related Models and Datasets
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* Base model: `guus4324343/Echo88-150M-Base`
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* Base dataset: `guus4324343/Echo88-Pretrain-1.17B`
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* Instruction dataset: `guus4324343/Echo88-Instruct-173K`
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## Bias and Historical Content
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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.
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Users should review outputs carefully.
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## License
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The model weights are released under the Apache 2.0 license.
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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.
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```
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 12,
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"num_hidden_layers": 18,
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"num_key_value_heads": 4,
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"pad_token_id": 1,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.7.0",
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"use_cache": false,
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"vocab_size": 32768
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 1,
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"transformers_version": "5.7.0",
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"use_cache": false
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:129f111d87350f7968e793066a21b2592bc61cc56c6e6431c3d48de2effd3080
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size 327231072
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sft_training_info.json
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sft_training_info.json
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{
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"base_model": "/content/echo88_final/Echo88-150M-Instruct",
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"instruct_dataset": "/content/echo88/echo88_fact_repair_20k.jsonl",
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"max_length": 1024,
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"micro_batch": 64,
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"grad_accum": 2,
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"epochs": 4,
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"learning_rate": 1e-05,
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"train_examples": 19500,
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"eval_examples": 500,
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"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."
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}
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tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"extra_special_tokens": [
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"<|system|>",
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"<|user|>",
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"<|assistant|>",
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"<|end|>"
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],
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<|unk|>"
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}
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training_args.bin
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:75a742724ffcbdc1864877d7b22149c4eaa266267320603881764b14f1172669
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size 5265
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