Instructions to use eadx/Kimi-K2.7-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eadx/Kimi-K2.7-Code-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True)
model = AutoModel.from_pretrained("eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True)

llama-cpp-python

How to use eadx/Kimi-K2.7-Code-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eadx/Kimi-K2.7-Code-GGUF",
	filename="UD-IQ1_M/Kimi-K2.7-Code-UD-IQ1_M-00001-of-00008.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eadx/Kimi-K2.7-Code-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use eadx/Kimi-K2.7-Code-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eadx/Kimi-K2.7-Code-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

SGLang

How to use eadx/Kimi-K2.7-Code-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "eadx/Kimi-K2.7-Code-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "eadx/Kimi-K2.7-Code-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use eadx/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use eadx/Kimi-K2.7-Code-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

How to use eadx/Kimi-K2.7-Code-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use eadx/Kimi-K2.7-Code-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use eadx/Kimi-K2.7-Code-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use eadx/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Lemonade

How to use eadx/Kimi-K2.7-Code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Kimi-K2.7-Code-GGUF / config.json

eadx

Duplicate from unsloth/Kimi-K2.7-Code-GGUF

d2c29db 21 days ago

Raw

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	{
	"architectures": [
	"KimiK25ForConditionalGeneration"
	],
	"auto_map": {
	"AutoConfig": "configuration_kimi_k25.KimiK25Config",
	"AutoModel": "modeling_kimi_k25.KimiK25ForConditionalGeneration",
	"AutoModelForCausalLM": "modeling_kimi_k25.KimiK25ForConditionalGeneration"
	},
	"bos_token_id": 163584,
	"torch_dtype": "bfloat16",
	"eos_token_id": 163586,
	"ignore_index": -100,
	"media_placeholder_token_id": 163605,
	"model_type": "kimi_k25",
	"pad_token_id": 163839,
	"quantization_config": {
	"config_groups": {
	"group_0": {
	"input_activations": null,
	"output_activations": null,
	"targets": [
	"Linear"
	],
	"weights": {
	"actorder": null,
	"block_structure": null,
	"dynamic": false,
	"group_size": 32,
	"num_bits": 4,
	"observer": "minmax",
	"observer_kwargs": {},
	"strategy": "group",
	"symmetric": true,
	"type": "int"
	}
	}
	},
	"format": "pack-quantized",
	"ignore": [
	"re:.self_attn.",
	"re:.shared_experts.",
	"re:.mlp\\.(gate\|up\|gate_up\|down)_proj.",
	"re:.lm_head.",
	"re:.vision_tower.",
	"re:.mm_projector."
	],
	"kv_cache_scheme": null,
	"quant_method": "compressed-tensors",
	"quantization_status": "compressed"
	},
	"text_config": {
	"_name_or_path": "",
	"add_cross_attention": false,
	"architectures": [
	"DeepseekV3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_deepseek.DeepseekV3Config",
	"AutoModel": "modeling_deepseek.DeepseekV3Model",
	"AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
	},
	"aux_loss_alpha": 0.001,
	"bos_token_id": 163584,
	"chunk_size_feed_forward": 0,
	"cross_attention_hidden_size": null,
	"decoder_start_token_id": null,
	"torch_dtype": "bfloat16",
	"eos_token_id": 163586,
	"ep_size": 1,
	"finetuning_task": null,
	"first_k_dense_replace": 1,
	"hidden_act": "silu",
	"hidden_size": 7168,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"initializer_range": 0.02,
	"intermediate_size": 18432,
	"is_decoder": false,
	"is_encoder_decoder": false,
	"kv_lora_rank": 512,
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"max_position_embeddings": 262144,
	"model_type": "deepseek_v3",
	"moe_intermediate_size": 2048,
	"moe_layer_freq": 1,
	"n_group": 1,
	"n_routed_experts": 384,
	"n_shared_experts": 1,
	"norm_topk_prob": true,
	"num_attention_heads": 64,
	"num_experts_per_tok": 8,
	"num_hidden_layers": 61,
	"num_key_value_heads": 64,
	"num_nextn_predict_layers": 0,
	"output_attentions": false,
	"output_hidden_states": false,
	"pad_token_id": 163839,
	"prefix": null,
	"pretraining_tp": 1,
	"problem_type": null,
	"pruned_heads": {},
	"q_lora_rank": 1536,
	"qk_nope_head_dim": 128,
	"qk_rope_head_dim": 64,
	"quantization_config": {
	"config_groups": {
	"group_0": {
	"input_activations": null,
	"output_activations": null,
	"targets": [
	"Linear"
	],
	"weights": {
	"actorder": null,
	"block_structure": null,
	"dynamic": false,
	"group_size": 32,
	"num_bits": 4,
	"observer": "minmax",
	"observer_kwargs": {},
	"strategy": "group",
	"symmetric": true,
	"type": "int"
	}
	}
	},
	"format": "pack-quantized",
	"ignore": [
	"re:.self_attn.",
	"re:.shared_experts.",
	"re:.mlp\\.(gate\|up\|gate_up\|down)_proj.",
	"re:.lm_head.",
	"re:.vision_tower.",
	"re:.mm_projector."
	],
	"kv_cache_scheme": null,
	"quant_method": "compressed-tensors",
	"quantization_status": "compressed"
	},
	"return_dict": true,
	"rms_norm_eps": 1e-05,
	"rope_parameters": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 64.0,
	"mscale": 1.0,
	"mscale_all_dim": 1.0,
	"original_max_position_embeddings": 4096,
	"rope_theta": 50000.0,
	"rope_type": "yarn",
	"type": "yarn"
	},
	"rope_theta": 50000.0,
	"routed_scaling_factor": 2.827,
	"scoring_func": "sigmoid",
	"sep_token_id": null,
	"seq_aux": true,
	"task_specific_params": null,
	"tf_legacy_loss": false,
	"tie_encoder_decoder": false,
	"tie_word_embeddings": false,
	"tokenizer_class": null,
	"topk_group": 1,
	"topk_method": "noaux_tc",
	"torchscript": false,
	"use_bfloat16": false,
	"use_cache": true,
	"v_head_dim": 128,
	"vocab_size": 163840
	},
	"tie_word_embeddings": false,
	"transformers_version": "5.13.0.dev0",
	"unsloth_fixed": true,
	"use_unified_vision_chunk": true,
	"video_placeholder": "<\|kimi_k25_video_placeholder\|>",
	"vision_config": {
	"init_pos_emb_height": 64,
	"init_pos_emb_time": 4,
	"init_pos_emb_width": 64,
	"merge_kernel_size": [
	2,
	2
	],
	"merge_type": "sd2_tpool",
	"mm_hidden_size": 1152,
	"mm_projector_type": "patchmerger",
	"model_type": "",
	"patch_size": 14,
	"pos_emb_type": "divided_fixed",
	"projector_hidden_act": "gelu",
	"projector_ln_eps": 1e-05,
	"text_hidden_size": 7168,
	"video_attn_type": "spatial_temporal",
	"vt_hidden_size": 1152,
	"vt_intermediate_size": 4304,
	"vt_num_attention_heads": 16,
	"vt_num_hidden_layers": 27
	}
	}