---

base_model: johnbean393/fluid-2-qwen3.5-2b-beta

library_name: llama.cpp

pipeline_tag: text-generation

tags:

• gguf
• qwen3.5
• fluid-2

---

Fluid 2 Qwen3.5 2B Beta — GGUF

Public GGUF conversions of the private step-344 Fluid 2 beta

checkpoint.

| File | Quantization | Size | SHA-256 |

|---|---:|---:|---|

| fluid-2-qwen3.5-2b-beta-Q4_K_M.gguf | Q4_K_M | 1,274,396,800 bytes | 36ba74f8298d69f7ffe4a50e554b41d0661bc1c3ef4dd2d3f521260fe4374bae |

| fluid-2-qwen3.5-2b-beta-Q6_K.gguf | Q6_K | 1,556,391,040 bytes | d009e51a8826527d707cefd6a12ad5390ab561e6678c854b472a6f1e051130ea |

| fluid-2-qwen3.5-2b-beta-Q8_0.gguf | Q8_0 | 2,012,012,672 bytes | 245c42169cdbfab6b5e879d41d75ef06e3316d891e3957f76d60ab1dc547e52d |

Corrected development-set evaluation

| Metric | Result |

|---|---:|

| Scored text rows | 7,016 |

| Exact match | 30.3449% |

| CER | 17.4199% |

| WER | 27.4544% |

| Excluded EOS-only rows | 121 |

| Excluded generation-capped rows | 24 |

EM, CER, and WER exclude both empty-target/EOS-only rows and non-empty-target

generations that reached the configured token cap. Capped requests remain in

the failure and throughput census. This public 7,161-row development set was

used during training and is not a blind-test result.

Prompt template

This is a dictation-cleaning completion model, not a conversational assistant.

Do not apply a chat template. Input must end immediately after

<|start_target_text|>; generation stops at <|end_target_text|>.

<|dictation_clean_v1|>
<|start_prev_text|>{previous context}<|end_prev_text|>
<|start_post_text|>{following context}<|end_post_text|>
<|start_asr_text|>{ASR transcript to clean}<|end_asr_text|>
<|start_target_text|>

Previous and following context may be empty, but keep all marker pairs.

Run with llama.cpp

Use a recent llama.cpp llama-completion binary. This example selects

Q4_K_M and uses greedy decoding:

PROMPT='<|dictation_clean_v1|>
<|start_prev_text|><|end_prev_text|>
<|start_post_text|><|end_post_text|>
<|start_asr_text|>hello world<|end_asr_text|>
<|start_target_text|>'

./llama-completion \
  --hf-repo johnbean393/fluid-2-qwen3.5-2b-beta-GGUF:Q4_K_M \
  --prompt "$PROMPT" \
  --predict 256 \
  --temperature 0 \
  --ctx-size 8192 \
  --no-conversation \
  --no-display-prompt

Use :Q6_K or :Q8_0 for another quant. For a local file, replace

--hf-repo ... with --model ./fluid-2-qwen3.5-2b-beta-Q6_K.gguf.

Add --special while debugging to display the terminal control token.

The files were converted with the matching b10411 converter and

quantized with the official pre-built Ubuntu x64 llama.cpp

b10411 release. No CUDA/source build was performed. Every quant

passed GGUF metadata validation and a load/generation smoke test with that

pre-built binary. Exact hashes are recorded in conversion_manifest.json.

MTP / NextN note

The source configuration declares one MTP (multi-token prediction), also known

as NextN, speculative draft layer. The causal-LM checkpoint itself contains

the 24 trained decoder layers and **does not contain any MTP/NextN

draft-layer tensors**. Default conversion would therefore advertise a

nonexistent extra block and fail when the runtime requests that tensor.

These GGUFs intentionally use --no-mtp. This omits only the absent optional

speculative draft layer; it does not remove trained decoder weights and does

not change ordinary next-token generation. The files correctly declare

24 blocks.