Hanno-Labs/dinghy-law-4b-v1-gguf overview
dinghy law 4b v1 — GGUF GGUF / llama.cpp build of Hanno Labs/dinghy law 4b v1 https://huggingface.co/Hanno Labs/dinghy law 4b v1 , a compact 0.6B legal text em…
Runs locally from ~2.33 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | Hanno-Labs/dinghy-law-4b-v1-gguf |
|---|---|
| Author | Hanno-Labs |
| Pipeline | — |
| License | apache-2.0 |
| Base model | Hanno-Labs/dinghy-law-4b-v1 |
| Last modified | 2026-07-22T17:48:08.000Z |
Model README
---
license: apache-2.0
base_model: Hanno-Labs/dinghy-law-4b-v1
tags:
- gguf
- llama.cpp
- embeddings
- legal
- sentence-transformers
library_name: gguf
---
dinghy-law-4b-v1 — GGUF
GGUF / llama.cpp build of Hanno-Labs/dinghy-law-4b-v1, a compact (0.6B) legal text-embedding model
(contrastive fine-tune of Qwen/Qwen3-Embedding-0.6B). Source revision d1fa2f346ca7461f680036b8af1927706a3afb58.
What this is (backbone-only)
The sentence-transformers pipeline is Transformer -> last-token Pooling -> Dense -> L2 Normalize. llama.cpp carries
the backbone: Transformer -> last-token pool -> L2 norm. The 2_Dense head is dropped — it is a
near-orthogonal rotation (normalized ortho-defect 0.00103, unit row-norms, ~zero bias), and cosine similarity is
invariant under rotation, so dropping it does not change retrieval ranking. Probe cosine(full, backbone) = 0.997 with
identical nearest-neighbor ordering.
Files
dinghy-law-4b-v1-Q4_K_M.gguf— 2.50 GBdinghy-law-4b-v1-Q6_K.gguf— 3.31 GBdinghy-law-4b-v1-Q8_0.gguf— 4.28 GBdinghy-law-4b-v1-f16.gguf— 8.05 GB
Fidelity — cosine(HF backbone, GGUF) on legal probes
| file | mean cos | min cos |
|---|---|---|
| dinghy-law-4b-v1-Q4_K_M.gguf | 0.97659 | 0.97009 |
| dinghy-law-4b-v1-Q6_K.gguf | 0.99553 | 0.99459 |
| dinghy-law-4b-v1-Q8_0.gguf | 0.99944 | 0.99931 |
| dinghy-law-4b-v1-f16.gguf | 1.00000 | 1.00000 |
Worst min-cos across quants: 0.97009 (WARN).
Usage (llama.cpp)
Embeddings use last-token pooling and L2 normalization:
llama-embedding -m dinghy-law-4b-v1-Q8_0.gguf \
-p "Retrieve statutes governing landlord obligations for habitability." \
--pooling last --embd-normalize 2
For retrieval, format the query with the Qwen3-Embedding instruction prefix (documents are embedded raw):
Instruct: {task_instruction}\nQuery: {query}
e.g. task instruction Identify the most relevant statutes for the given situation. for statute retrieval.
Provenance
Quantized from the source safetensors with llama.cpp/convert_hf_to_gguf.py (f16) + llama-quantize (Q8_0, Q4_K_M).
MTEB(Law, v1) Mean(Task) nDCG@10 of the full model = 71.24. License: apache-2.0.
Run Hanno-Labs/dinghy-law-4b-v1-gguf with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models