Lizzy 7B is an open-weight Flower Labs assistant model in the Lizzy family.
Architecture And Configuration
Lizzy 7B is a 7B-class decoder-only transformer with long-context support, sliding/local attention behaviour, custom chat/control tokens, and deployment-specific serving configurations.
Representative configuration points:
7B-class parameter scale with a 32-layer stack;
long-context configuration up to 65k tokens with runtime caps adjusted by deployment profile;
32 attention heads with long-context/sliding-attention behaviour;
custom tokenizer and chat markers for instruction-style prompting;
deployment variants may include quantised revisions, runtime patches, and serving-time configuration changes.
Training Approach
Lizzy 7B follows a multi-stage training approach that combines:
pre-training on large-scale public text, document, code, math, and encyclopedic corpora;
supervised fine-tuning on instruction-following, dialogue, reasoning, and tool-use examples;
direct preference optimisation on preference pairs for helpfulness, style, and answer quality;
reinforcement learning with verifiable rewards for targeted behavioural refinement.
Across these stages, training data has been mixed across:
broad public text and knowledge sources;
synthetic instruction and preference data;
private synthetic data used to favour British behaviour and knowledge;
UK-specific examples and preference signals used to strengthen local knowledge and style.
Evaluation Against European Baselines
Britishness comparisons against the European baselines present in the latest local artifact set:
Benchmark
Lizzy 7B
EuroLLM 9B
Apertus 8B
Britishness MCQ
71.0
77.6
80.8
Britishness CoT
80.1
72.1
31.7
Britishness Domains
89.9
69.0
32.6
Broader benchmark comparisons against the same European baselines:
Benchmark
Lizzy 7B
EuroLLM 9B
Apertus 8B
MATH
77.9
31.3
22.4
OMEGA
29.0
4.7
5.0
BigBenchHard
69.0
38.9
42.4
AGI Eval English
65.6
50.2
50.4
MMLU
67.9
57.4
63.4
GPQA
34.6
26.8
28.1
HumanEvalPlus
70.2
28.2
33.4
MBPP+
52.5
41.7
42.3
LiveCodeBench v3
39.1
6.3
8.5
IFEval
63.8
55.8
65.1
AIME
35.8
0.2
0.6
GSM8K
91.8
64.7
64.7
IFBench
22.7
18.0
15.3
POPQA
22.2
25.6
25.1
ZebraLogic
12.4
4.4
5.9
Summary:
Lizzy 7B trails the European baselines on Britishness MCQ (a private Flower Labs benchmark) recall-style probing.
Lizzy 7B leads the reported European baselines on Britishness CoT and Britishness domain reasoning (private Flower Labs benchmarks) where comparable metrics are available.
Lizzy 7B also leads the latest local European baseline set on most knowledge, reasoning, math, and coding rows represented in the table above.
Intended Uses And Limitations
Intended uses:
UK-oriented assistant experiences;
general reasoning and coding assistance;
managed deployment through private Hugging Face or vLLM serving stacks.
Safety And Bias Considerations
The latest safety-evaluation reports the following task-level primary scores:
Safety benchmark
Metric
Score
Overall safety average
overall_safety_average
66.7%
WildGuardTest
inverted_micro_harm_lower
91.9%
HarmBench
inverted_micro_asr_lower
57.5%
ToxiGen (tiny)
safe_overall
90.2%
XSTest
overall_accuracy
85.6%
StrongReject (logprobs)
inverted_asr
78.8%
BBQ
accuracy
66.5%
WMDP
inverted_accuracy
47.5%
Lizzy 7B can still produce incorrect, outdated, or over-confident responses and should be used with human oversight for higher-risk workflows. UK-specific tuning improves local style and cultural alignment but can also bias tone and assumptions toward UK conventions; downstream moderation and policy controls remain required.
License And Citation
Model licence: Apache-2.0
Public and synthetic training sources include open-licensed public data plus private synthetic and UK-specific data that are not redistributed
Citation and legal text should still be confirmed by owner review before any external publication.
