初始化项目，由ModelHub XC社区提供模型
Model: flwrlabs/Lizzy-7B Source: Original Platform
This commit is contained in:
ModelHub XC
174
README.md
Normal file
174
README.md
Normal file
@@ -0,0 +1,174 @@
|
||||
---
|
||||
language:
|
||||
- en
|
||||
library_name: transformers
|
||||
pipeline_tag: text-generation
|
||||
license: apache-2.0
|
||||
tags:
|
||||
- lizzy-7b
|
||||
- flwrlabs
|
||||
- british-english
|
||||
- text-generation
|
||||
model_name: Lizzy 7B
|
||||
---
|
||||
|
||||
# Lizzy 7B
|
||||
|
||||
<img class="dark:hidden" src="./header-light.svg" alt="Lizzy 7B header figure (light theme)" />
|
||||
<img class="hidden dark:block" src="./header-dark.svg" alt="Lizzy 7B header figure (dark theme)" />
|
||||
|
||||
## 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:
|
||||
|
||||
| Benchmark | Lizzy 7B | EuroLLM 9B | Apertus 8B |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| Britishness MCQ | 71.0 | <u>77.6</u> | **80.8** |
|
||||
| Britishness CoT | **80.1** | <u>72.1</u> | 31.7 |
|
||||
| Britishness Domains | **89.9** | <u>69.0</u> | 32.6 |
|
||||
|
||||
Broader benchmark comparisons against the same European baselines:
|
||||
|
||||
| Benchmark | Lizzy 7B | EuroLLM 9B | Apertus 8B |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| MATH | **77.9** | <u>31.3</u> | 22.4 |
|
||||
| OMEGA | **29.0** | 4.7 | <u>5.0</u> |
|
||||
| BigBenchHard | **69.0** | 38.9 | <u>42.4</u> |
|
||||
| AGI Eval English | **65.6** | 50.2 | <u>50.4</u> |
|
||||
| MMLU | **67.9** | 57.4 | <u>63.4</u> |
|
||||
| GPQA | **34.6** | 26.8 | <u>28.1</u> |
|
||||
| HumanEvalPlus | **70.2** | 28.2 | <u>33.4</u> |
|
||||
| MBPP+ | **52.5** | 41.7 | <u>42.3</u> |
|
||||
| LiveCodeBench v3 | **39.1** | 6.3 | <u>8.5</u> |
|
||||
| IFEval | <u>63.8</u> | 55.8 | **65.1** |
|
||||
| AIME | **35.8** | 0.2 | <u>0.6</u> |
|
||||
| GSM8K | **91.8** | <u>64.7</u> | 64.7 |
|
||||
| IFBench | **22.7** | <u>18.0</u> | 15.3 |
|
||||
| POPQA | 22.2 | **25.6** | <u>25.1</u> |
|
||||
| ZebraLogic | **12.4** | 4.4 | <u>5.9</u> |
|
||||
|
||||
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.
|
||||
|
||||
## Python Example (Transformers)
|
||||
|
||||
```python
|
||||
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
|
||||
Reference in New Issue
Block a user