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Model: adilkairolla/zeta-2.1-GGUF Source: Original Platform
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README.md
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---
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base_model: zed-industries/zeta-2.1
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base_model_relation: quantized
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quantized_by: adilkairolla
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license: apache-2.0
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- gguf
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- llama.cpp
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- quantized
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- edit-prediction
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- next-edit-suggestion
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- code
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language:
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- en
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---
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# Zeta 2.1 — GGUF
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GGUF quantizations of [`zed-industries/zeta-2.1`](https://huggingface.co/zed-industries/zeta-2.1), a code edit prediction (next-edit suggestion) model from [Zed Industries](https://zed.dev).
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These files were produced from the original BF16 safetensors using [llama.cpp `b9085`](https://github.com/ggml-org/llama.cpp/releases/tag/b9085) (`convert_hf_to_gguf.py` → `llama-quantize`). No fine-tuning or weight modification beyond format conversion and quantization.
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## Files
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| Quant | Size | Notes |
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|-----------|-------:|-----------------------------------------------------------------------|
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| `Q4_K_M` | 4.8 GB | Smallest, recommended default for CPU / 8 GB-class GPUs. |
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| `Q5_K_M` | 5.5 GB | Quality / size sweet spot. |
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| `Q8_0` | 8.2 GB | Near-lossless vs. the original BF16 weights. |
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| `f16` | 16 GB | Reference. Useful as the source for further quantization. |
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## Quickstart
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### Ollama
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```bash
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ollama pull hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M
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```
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Replace the tag with `Q5_K_M`, `Q8_0`, or `f16` for a different quant. Zeta is a code-edit-prediction model (not a chat model) — call it via `/api/generate` with the FIM prompt below, **not** via `/api/chat`.
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### LM Studio
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Search for `adilkairolla/zeta-2.1-GGUF` in the model browser and pick a quant. LM Studio loads it as a base completion model.
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### llama.cpp
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```bash
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# One-shot completion (correct binary for non-chat models)
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./llama-completion -m zeta-2.1-Q4_K_M.gguf -p "$(cat your-prompt.txt)" -n 256 -c 4096
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```
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### llama-cpp-python
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="zeta-2.1-Q4_K_M.gguf", n_ctx=4096)
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out = llm(prompt, max_tokens=256, stop=["<|marker_2|>"], echo=False)
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print(out["choices"][0]["text"])
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```
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## Prompt format
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Zeta uses a Suffix-Prefix-Middle (SPM) FIM format with numbered region markers. Quoting the upstream model card:
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```
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<[fim-suffix]>
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code after editable region
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<[fim-prefix]><filename>related/file.py
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related file content
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<filename>edit_history
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--- a/some_file.py
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+++ b/some_file.py
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-old
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+new
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<filename>path/to/target_file.py
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code before editable region
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<|marker_1|>
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code that
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needs to<|user_cursor|>
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be rewritten
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<|marker_2|>
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<[fim-middle]>
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```
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Expected output:
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```
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<|marker_1|>
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revised content for
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the editable region
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<|marker_2|>
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```
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See the upstream [`sample.prompt`](https://huggingface.co/zed-industries/zeta-2.1/blob/main/sample.prompt) and [`sample.output`](https://huggingface.co/zed-industries/zeta-2.1/blob/main/sample.output) for a real example.
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## Source & lineage
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- **Quantized from:** [`zed-industries/zeta-2.1`](https://huggingface.co/zed-industries/zeta-2.1) (BF16 safetensors)
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- **Fine-tuned from:** [`ByteDance-Seed/Seed-Coder-8B-Base`](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base)
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- **Architecture:** Llama (32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 155136, RoPE θ 500000, ctx 32768)
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- **Conversion tool:** llama.cpp `b9085`
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## License
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Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), inherited from the upstream model. The only modification relative to upstream is the conversion to GGUF and quantization to the formats listed above.
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All credit for the model itself goes to Zed Industries and ByteDance-Seed. This repo is an unaffiliated quantization mirror.
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zeta-2.1-Q4_K_M.gguf
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size 5071409664
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zeta-2.1-Q5_K_M.gguf
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