Skip to content
All models

Model card · Public experimental release

quantum-1.6-pilot

49.3MParameter size

49,295,872 parameters

Published architecture

8h12 layersd = 512

Summary

A 49.3M-parameter experimental German completion model released after a reported continued-pretraining stage.

Status
Public experimental release
Model type
LlamaForCausalLM-style experimental completion model
Intended use
Research into documented continued-pretraining workflows · Local German-language completion experiments
Languages
German
Lineage
The release card reports continued pretraining from Quantum 1 Base. The public configuration specifies weights-only initialization, a fresh optimizer, scheduler and step counter, and a frozen quantum-1 tokenizer; no final public run manifest verifies every configured detail.
Release status
Publicly available as an experimental F16 GGUF release; the public artifact is 98,990,560 bytes.
License
No model license is currently stated in the public repository. Downloadability does not by itself define reuse rights.

Technical dossier

Documented model facts.

Information linked to public project sources. Unknown release dates, licensing terms and unsupported client compatibility are not inferred.

Version
1.6.0, matching the public quantum-1.6-pilot-v1.6.0-f16.gguf filename and release manifest
Architecture
LlamaForCausalLM-style; hidden size 512; intermediate size 1,536; 12 layers; 8 attention heads; 8 KV heads; tied embeddings
Vocabulary
16,384 tokens
Context
512 tokens
Tokenizer
Custom frozen quantum-1 tokenizer with a 16,384-token vocabulary
Training data
Approximately 100M German base-training tokens plus 500M additional German tokens, as documented in the public model card.
Training configuration
The public configuration targets approximately 30,518 steps, derived from 500M tokens at an effective 16,384 tokens per step. A final public run log is not linked.
Evaluation
The Hugging Face model card reports validation loss 3.348852 and perplexity 28.4700. No versioned evaluation report or standardized downstream-task benchmarks have been published.
Release
F16 GGUF, approximately 99 MB
Quantization
The public release provides an F16 GGUF; no Q8 or Q4 variants are listed
Hardware
Minimum RAM requirements have not been formally measured
Inference
llama.cpp compatibility is documented. Android comparison tooling exists, but no completed public Android validation report is currently linked.

Primary sources:Hugging Face model card ·Training documentation ·Generation diagnosis

Reference completion command

llama-completion -m quantum-1.6-pilot-v1.6.0-f16.gguf -p "Berlin ist" -n 64 --temp 0 --top-p 1 --top-k 0

Completion prompting is the documented reference path. No chat template is promised.

Research context

How this release fits the experiment.

This release reports continued pretraining with a fixed architecture and tokenizer. Public configuration and code are available, but a final run manifest and complete training logs are not linked.

Repository reference

The pilot model card.

A visual snapshot of the model's parameter size and intended research use.

MODEL CARD

rappidAI · research

quantum-1.6-pilot

Parameter size

49.3M

parameters

49.3M released parameters against the 506M configured Echelon target.

Primary use

Research into documented continued-pretraining workflows

Model type
LlamaForCausalLM-style experimental completion model
Context
512 tokens
Vocabulary
16,384

RELATED RESOURCES

Inspect the wider evidence.

Limitations

Read the limits first.

01

Semantically weak and factually unreliable output

02

Outputs may be incomplete or incoherent

03

Limited to a 512-token context window

04

No instruction tuning or chat alignment

05

No standardized task benchmarks have been published

06

Not suitable for medicine, law, finance, safety or other high-stakes applications

07

Not intended as a general production assistant

08

Third-party client compatibility has not been established by a published validation report

This model is experimental and not production-ready. It must not be used for high-stakes decisions.