Lizzy 7B

Model Name And Summary

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:

Broader benchmark comparisons against the same European baselines:

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:

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.

Python Example (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo_id = "flwrlabs/Lizzy-7B"

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

messages = [
    {"role": "system", "content": "You are Lizzy 7B."},
    {"role": "user", "content": "Summarise why queue etiquette matters in the UK."},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

output_ids = model.generate(
    **inputs,
    temperature=0.2,
    top_p=0.9,
)
response = tokenizer.decode(output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)

Multi-GPU vLLM Tensor Parallel Patch

For reproducible multi-GPU vLLM support with Lizzy-family checkpoints, this deliverable bundles:

• bundled draft artifact: vllm_patches/transformers_lizzy_tp.py

Apply this patch when all of the following are true:

• runtime uses vLLM via the generic Transformers backend (model_type=vllm)
• tensor parallelism is enabled (tensor_parallel_size > 1)
• checkpoint is Lizzy-family (including RLVR variants)
• runtime is not guaranteed to include an equivalent upstream fix

You can skip patch bundling only for strict HF-only runs or single-rank vLLM (TP=1).

Why this is included:

• it mitigates known Lizzy TP failure modes in generic vLLM Transformers loading
• it fixes rank-local head partitioning and q_norm/k_norm slicing behaviour
• it prevents the known tensor-shape crash class seen without this patch