---

license: other

base_model: moonshotai/Kimi-K2.7-Code

tags:

• gguf
• llama.cpp
• kimi
• kimi-k2.7-code
• bf16
• quantized
• tq1_0
• tq2_0
• q4_k_m
• q8_0

library_name: gguf

---

Kimi K2.7 Code GGUF

GGUF conversions of moonshotai/Kimi-K2.7-Code for llama.cpp-compatible runtimes.

Available files

| Variant | Files | Approx local size | Status | Notes |

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

| Q8_0 | Q8_0/kimi-k2.7-code-Q8_0-00001-of-00061.gguf ... 00061-of-00061.gguf | ~1017 GiB | Uploaded | 8-bit GGUF, split into 61 shards. |

| Q4_K_M | Q4_K_M/kimi-k2.7-code-Q4_K_M-00001-of-00061.gguf ... 00061-of-00061.gguf | ~578 GiB | Uploaded | Standard high-quality 4-bit GGUF, split because the single file exceeds Hugging Face’s 500GB per-file limit. |

| TQ2_0 | TQ2_0/kimi-k2.7-code-TQ2_0.gguf | ~249 GiB | Uploaded | 2-bit-class ternary quantization, single file. |

| TQ1_0 | TQ1_0/kimi-k2.7-code-TQ1_0.gguf | ~204 GiB | Uploaded | 1-bit-class ternary quantization, single file. |

Q6_K is not currently uploaded in this repository.

Loading split GGUF files

For split GGUF variants, download all shards for the variant into the same directory and point llama.cpp at shard 00001. llama.cpp will discover the remaining shards automatically.

Examples:

# BF16
llama-cli -m BF16/kimi-k2.7-code-BF16-00001-of-00061.gguf -p "Write a Python function for quicksort."

# Q8_0
llama-cli -m Q8_0/kimi-k2.7-code-Q8_0-00001-of-00061.gguf -p "Write a Rust HTTP server."

# Q4_K_M
llama-cli -m Q4_K_M/kimi-k2.7-code-Q4_K_M-00001-of-00061.gguf -p "Explain async/await."

Single-file variants can be loaded directly:

llama-cli -m TQ2_0/kimi-k2.7-code-TQ2_0.gguf -p "Hello"
llama-cli -m TQ1_0/kimi-k2.7-code-TQ1_0.gguf -p "Hello"

Quantization notes

• BF16 was converted from the original SafeTensors using llama.cpp convert_hf_to_gguf.py with BF16 output.
• Q8_0 and Q4_K_M were quantized from the BF16 GGUF source and uploaded as split GGUF shards.
• TQ1_0 and TQ2_0 are llama.cpp ternary low-bit formats.
• IQ1_S was not produced because llama.cpp requires an importance matrix for that quantization.
• Very large variants are split to stay under Hugging Face’s individual file-size limit.

License

See LICENSE. This model uses Moonshot AI’s Modified MIT License for Kimi K2.7 Code.

Attribution

Base model by Moonshot AI: moonshotai/Kimi-K2.7-Code.