---

library_name: gguf

license: apache-2.0

language:

- eu

- en

- es

tags:

- gguf

- llama.cpp

- mamba-2

- basque

- autocomplete

- fim

- fill-in-the-middle

- ghost-text

- low-resource

base_model: itzune/morpheus

pipeline_tag: text-generation

---

Morpheus (Mamba-2) — Basque FIM Autocomplete (GGUF)

GGUF quantized version of Morpheus v3, a 91M-parameter Mamba-2 language model for Basque (Euskara) text autocompletion with Fill-in-the-Middle (FIM) support — the model can complete text at the cursor, not just at the end.

This is a continued-pretraining of the AR-only v2 model (step 74K) on FIM-structured data.

What's new in v3 (FIM)

| Capability | v2 (AR-only) | v3 (FIM) |

|------------|--------------|----------|

| Next-word / end-of-line completion | ✅ | ✅ |

| Fill-in-the-Middle (cursor mid-text) | ❌ | ✅ |

| Special tokens | 4000 | 4004 (<PRE> <SUF> <MID> <EOT>) |

| Vocab (padded) | 4016 | 4016 |

FIM training recipe (Phase 6 v3)

• Base: v2 best.pt (AR checkpoint, step 3500 of Phase 6 v2)
• Data: 500M tokens, 70/30 FIM/AR ratio (BigCode-style token-level splitting)
• EOT loss weighting: 5× on the <EOT> stop token — forces the model to learn when to stop generating infill, not just what to generate
• Packed dataset: Greedy packing of whole examples into 1025-token windows (no cross-example contamination)
• LR: 1.0e-3, cosine decay, ~3,815 steps
• Tokenizer: basque_unigram_fim.model (original 4000 pieces + 4 FIM tokens)

The 70/30 ratio + 5× EOT weight was selected after a senior ML engineer review of the v2 failure mode (unreliable <EOT> emission → over-generation). See Option D in the analysis below.

Available Files

| File | Quant | Size | Use |

|------|-------|------|-----|

| v3_fim.Q5_K_M.gguf | Q5_K_M | 64 MB | Recommended — FIM + AR |

| morpheus-v2-mamba.Q4_K_M.gguf | Q4_K_M | 53 MB | AR-only (legacy v2) |

| morpheus-v2-mamba.Q5_K_M.gguf | Q5_K_M | 64 MB | AR-only (legacy v2) |

FIM Evaluation Results

Trained-for-behavior eval on 147 held-out FIM examples (token-level splits, 20% at linguistic boundaries):

| Metric | v2 (50/50, no EOT weight) | v3 (70/30, 5× EOT weight) | Change |

|--------|---------------------------|---------------------------|--------|

| EOT emission rate | 76.9% | 88.4% | +11.5pp |

| Keystrokes saved | -25.2% | -5.9% | 4× better |

| Exact match | 2.7% | 6.8% | 2.5× |

| Char accuracy | 31.2% | 32.3% | +1.1pp |

| Gen length (ref=45.0) | 56.8 (over-gen) | 40.3 | closer to target |

| Prefix truncation | 0.0% | 1.4% | minimal |

| └ long-bucket truncation | 0.0% | 2.3% | well under 15% threshold |

Key findings:

• The 5× EOT loss weighting worked as designed: EOT reliability up 77% → 88%, and the model stopped over-generating (56.8 → 40.3 chars vs 45.0 target).
• The feared failure mode — premature truncation from over-weighting EOT — did not materialize (1.4% overall, 2.3% in the long bucket; failure threshold was 15%).
• AR perplexity is stable (7.4 → 7.5) — "FIM-for-free" property holds even with 70% FIM data.

Model Details

• Architecture: Mamba-2 (State Space Model), 24 layers, d_model 768
• Parameters: 91M
• Checkpoint: Phase 6 v3 best.pt (step 3500)
• AR perplexity: 7.5 | FIM perplexity: 7.9
• Tokenizer: basque_unigram_fim.model — 4004 pieces (original 4000 + <PRE> <SUF> <MID> <EOT>)
• Trained without BOS token (add_bos_token=false)

FIM Token Format

The model uses Code Llama-style FIM tokens. To do a fill-in-the-middle completion, structure the prompt as:

<PRE>{prefix}<SUF>{suffix}<MID>

The model generates the infill and emits <EOT> when done.

| Token | ID | Purpose |

|-------|----|---------|

| <PRE> | 4000 | Marks start of prefix |

| <SUF> | 4001 | Marks start of suffix |

| <MID> | 4002 | Marks start of generation (infill) |

| <EOT> | 4003 | End-of-infill (stop token) |

| 4004–4015 | — | Padding (unused) |

Usage

With the Morpheus demo server (recommended)

The Morpheus demo includes a FastAPI proxy that handles FIM templating, token-ID encoding, and an OpenAI-compatible API:

cd demo
docker compose -f docker-compose.yml -f docker-compose.local.yml up -d --build
# Open http://localhost:9090/editor.html

Set MORPHEUS_MODEL=v3_fim.Q5_K_M.gguf to use this model.

Direct llama-server (raw FIM)

llama-server -m v3_fim.Q5_K_M.gguf --host 127.0.0.1 --port 8080 -ngl 0 --threads 8

# FIM completion:
curl http://localhost:8080/completion \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "<PRE>Kaixo, <SUF> moduz?<MID>",
    "n_predict": 20,
    "temperature": 0.2,
    "top_k": 5,
    "stop": ["<EOT>"]
  }'

⚠️ Critical: token-ID prompts, not strings

As with v2, deploy via token-ID prompts (encode with sentencepiece using basque_unigram_fim.model, send IDs to /completion). The proxy handles this automatically. See the v2 model card for the full explanation.

Decoding parameters (recommended)

These are wired into the demo proxy and editor:

| Parameter | Value | Rationale |

|-----------|-------|-----------|

| temperature | 0.2 | Low-but-nonzero: recovers rank-2 correct tokens that greedy misses |

| top_k | 5 | Small nucleus; P7 finding: 5 correct answers sit at rank 2 in top-5 |

| stop (FIM) | ["<EOT>", "\n\n"] | Model emits <EOT>; fallback to paragraph boundary |

| repeat_penalty (FIM) | 1.0 | FIM legitimately reuses context words — do not penalize |

Intended Use

Desktop text-editor ghost-text autocompletion for Basque prose. The Mamba-2 architecture's O(1) decode cost makes it well-suited to long editing sessions where latency per token matters more than parallelism.

Not intended for: instruction following, chat, translation, or factual QA. This is a narrow autocomplete model.

License

Apache-2.0.

Citation

Morpheus v3 — Mamba-2 FIM Autocomplete for Basque.
itzune/morpheus-gguf, 2026.