Instructions to use bartowski/OmniJev_OneJev-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bartowski/OmniJev_OneJev-9B-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 bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

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 bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

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 bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use bartowski/OmniJev_OneJev-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/OmniJev_OneJev-9B-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": "bartowski/OmniJev_OneJev-9B-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/bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Ollama
How to use bartowski/OmniJev_OneJev-9B-GGUF with Ollama:
```
ollama run hf.co/bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bartowski/OmniJev_OneJev-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/OmniJev_OneJev-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/OmniJev_OneJev-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OmniJev_OneJev-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/OmniJev_OneJev-9B-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 bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

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 bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/OmniJev_OneJev-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M

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 "bartowski/OmniJev_OneJev-9B-GGUF:Q4_K_M" \
  --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"

OmniJev_OneJev-9B-GGUF / layouts /OmniJev_OneJev-9B-IQ3_XS.layout.json

bartowski

Upload folder using huggingface_hub

6e374cf verified 27 days ago

Raw History Blame Contribute Delete

7.45 kB

{
  "model": "/media/m2teamgroup/models/OneJev-9B-GGUF/OmniJev_OneJev-9B-bf16.gguf",
  "imatrix": "/media/m2teamgroup/models/OneJev-9B-GGUF/OmniJev_OneJev-9B-imatrix.gguf",
  "ftype": "IQ3_XS",
  "base_type": "iq3_s",
  "family": "iq",
  "recipe": "ident",
  "variant": "shr0.90",
  "auto_crush": false,
  "rung_mode": "share",
  "tier": "S",
  "rung_share": 0.9,
  "crush_pass_bytes": null,
  "embd_shares": {
    "output.weight": {
      "share": 0.1834,
      "type": "q4_k"
    },
    "token_embd.weight": {
      "share": 0.0961,
      "type": "q4_k"
    }
  },
  "arch": "hybrid",
  "tied": false,
  "n_layer": 32,
  "body_rule": true,
  "pins": {
    "^blk\\.0\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.0\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.1\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.1\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.2\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.2\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.4\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.4\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.5\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.5\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.6\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.6\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.8\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.8\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.9\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.9\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.10\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.10\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.12\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.12\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.13\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.13\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.14\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.14\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.16\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.16\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.17\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.17\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.18\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.18\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.20\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.20\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.21\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.21\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.22\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.22\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.24\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.24\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.25\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.25\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.26\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.26\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.28\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.28\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.29\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.29\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.30\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
    "^blk\\.30\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32"
  },
  "shape_pins": {},
  "fixed_pins": {
    "blk.0.ssm_alpha.weight": "f32",
    "blk.0.ssm_beta.weight": "f32",
    "blk.1.ssm_alpha.weight": "f32",
    "blk.1.ssm_beta.weight": "f32",
    "blk.2.ssm_alpha.weight": "f32",
    "blk.2.ssm_beta.weight": "f32",
    "blk.4.ssm_alpha.weight": "f32",
    "blk.4.ssm_beta.weight": "f32",
    "blk.5.ssm_alpha.weight": "f32",
    "blk.5.ssm_beta.weight": "f32",
    "blk.6.ssm_alpha.weight": "f32",
    "blk.6.ssm_beta.weight": "f32",
    "blk.8.ssm_alpha.weight": "f32",
    "blk.8.ssm_beta.weight": "f32",
    "blk.9.ssm_alpha.weight": "f32",
    "blk.9.ssm_beta.weight": "f32",
    "blk.10.ssm_alpha.weight": "f32",
    "blk.10.ssm_beta.weight": "f32",
    "blk.12.ssm_alpha.weight": "f32",
    "blk.12.ssm_beta.weight": "f32",
    "blk.13.ssm_alpha.weight": "f32",
    "blk.13.ssm_beta.weight": "f32",
    "blk.14.ssm_alpha.weight": "f32",
    "blk.14.ssm_beta.weight": "f32",
    "blk.16.ssm_alpha.weight": "f32",
    "blk.16.ssm_beta.weight": "f32",
    "blk.17.ssm_alpha.weight": "f32",
    "blk.17.ssm_beta.weight": "f32",
    "blk.18.ssm_alpha.weight": "f32",
    "blk.18.ssm_beta.weight": "f32",
    "blk.20.ssm_alpha.weight": "f32",
    "blk.20.ssm_beta.weight": "f32",
    "blk.21.ssm_alpha.weight": "f32",
    "blk.21.ssm_beta.weight": "f32",
    "blk.22.ssm_alpha.weight": "f32",
    "blk.22.ssm_beta.weight": "f32",
    "blk.24.ssm_alpha.weight": "f32",
    "blk.24.ssm_beta.weight": "f32",
    "blk.25.ssm_alpha.weight": "f32",
    "blk.25.ssm_beta.weight": "f32",
    "blk.26.ssm_alpha.weight": "f32",
    "blk.26.ssm_beta.weight": "f32",
    "blk.28.ssm_alpha.weight": "f32",
    "blk.28.ssm_beta.weight": "f32",
    "blk.29.ssm_alpha.weight": "f32",
    "blk.29.ssm_beta.weight": "f32",
    "blk.30.ssm_alpha.weight": "f32",
    "blk.30.ssm_beta.weight": "f32"
  },
  "embeddings": {
    "output.weight": "q4_k",
    "token_embd.weight": "q4_k"
  },
  "bytes": {
    "heuristic_body+embd": 4520148992,
    "map_body+embd": 4227661824,
    "ratio": 0.9353,
    "note": "quantizable, unpinned tensors only; pinned/unquantized tensors are identical in both"
  },
  "body_base_share": 0.9,
  "body_bpw": 3.569,
  "body_bpw_heuristic": 3.76,
  "total_params": 8953803264,
  "file_bytes_pred": 4257073152,
  "file_bpw_pred": 3.804,
  "histogram": {
    "attn_gate": {
      "iq3_s": 23,
      "q5_k": 1
    },
    "attn_k": {
      "q5_k": 7,
      "q4_k": 1
    },
    "attn_output": {
      "q5_k": 8
    },
    "attn_q": {
      "iq3_s": 8
    },
    "attn_qkv": {
      "iq3_s": 23,
      "q5_k": 1
    },
    "attn_v": {
      "q5_k": 6,
      "q6_k": 2
    },
    "ffn_down": {
      "iq3_s": 31,
      "q5_k": 1
    },
    "ffn_gate": {
      "iq3_s": 32
    },
    "ffn_up": {
      "iq3_s": 31,
      "q5_k": 1
    },
    "ssm_out": {
      "iq3_s": 18,
      "q4_k": 3,
      "q5_k": 2,
      "q6_k": 1
    }
  },
  "prior": "layout/prior.json",
  "pin_rule": "floor",
  "embd_cap": "heur",
  "generator": "auto_quant_v2.layout",
  "generator_key": "3bf8b43e20a68f0b",
  "quant_type": "IQ3_XS",
  "quantize_ftype": "IQ3_XS",
  "llama_cpp_version": "b11259",
  "commit": "24687c01f2f1e36eca618e35f7d5b4fc3ddd4dc2"
}

	{
	"model": "/media/m2teamgroup/models/OneJev-9B-GGUF/OmniJev_OneJev-9B-bf16.gguf",
	"imatrix": "/media/m2teamgroup/models/OneJev-9B-GGUF/OmniJev_OneJev-9B-imatrix.gguf",
	"ftype": "IQ3_XS",
	"base_type": "iq3_s",
	"family": "iq",
	"recipe": "ident",
	"variant": "shr0.90",
	"auto_crush": false,
	"rung_mode": "share",
	"tier": "S",
	"rung_share": 0.9,
	"crush_pass_bytes": null,
	"embd_shares": {
	"output.weight": {
	"share": 0.1834,
	"type": "q4_k"
	},
	"token_embd.weight": {
	"share": 0.0961,
	"type": "q4_k"
	}
	},
	"arch": "hybrid",
	"tied": false,
	"n_layer": 32,
	"body_rule": true,
	"pins": {
	"^blk\\.0\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.0\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.1\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.1\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.2\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.2\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.4\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.4\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.5\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.5\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.6\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.6\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.8\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.8\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.9\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.9\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.10\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.10\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.12\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.12\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.13\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.13\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.14\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.14\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.16\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.16\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.17\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.17\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.18\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.18\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.20\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.20\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.21\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.21\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.22\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.22\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.24\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.24\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.25\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.25\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.26\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.26\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.28\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.28\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.29\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.29\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.30\\.ssm_alpha\\.weight$": "tiny: 131,072 params, kept at f32",
	"^blk\\.30\\.ssm_beta\\.weight$": "tiny: 131,072 params, kept at f32"
	},
	"shape_pins": {},
	"fixed_pins": {
	"blk.0.ssm_alpha.weight": "f32",
	"blk.0.ssm_beta.weight": "f32",
	"blk.1.ssm_alpha.weight": "f32",
	"blk.1.ssm_beta.weight": "f32",
	"blk.2.ssm_alpha.weight": "f32",
	"blk.2.ssm_beta.weight": "f32",
	"blk.4.ssm_alpha.weight": "f32",
	"blk.4.ssm_beta.weight": "f32",
	"blk.5.ssm_alpha.weight": "f32",
	"blk.5.ssm_beta.weight": "f32",
	"blk.6.ssm_alpha.weight": "f32",
	"blk.6.ssm_beta.weight": "f32",
	"blk.8.ssm_alpha.weight": "f32",
	"blk.8.ssm_beta.weight": "f32",
	"blk.9.ssm_alpha.weight": "f32",
	"blk.9.ssm_beta.weight": "f32",
	"blk.10.ssm_alpha.weight": "f32",
	"blk.10.ssm_beta.weight": "f32",
	"blk.12.ssm_alpha.weight": "f32",
	"blk.12.ssm_beta.weight": "f32",
	"blk.13.ssm_alpha.weight": "f32",
	"blk.13.ssm_beta.weight": "f32",
	"blk.14.ssm_alpha.weight": "f32",
	"blk.14.ssm_beta.weight": "f32",
	"blk.16.ssm_alpha.weight": "f32",
	"blk.16.ssm_beta.weight": "f32",
	"blk.17.ssm_alpha.weight": "f32",
	"blk.17.ssm_beta.weight": "f32",
	"blk.18.ssm_alpha.weight": "f32",
	"blk.18.ssm_beta.weight": "f32",
	"blk.20.ssm_alpha.weight": "f32",
	"blk.20.ssm_beta.weight": "f32",
	"blk.21.ssm_alpha.weight": "f32",
	"blk.21.ssm_beta.weight": "f32",
	"blk.22.ssm_alpha.weight": "f32",
	"blk.22.ssm_beta.weight": "f32",
	"blk.24.ssm_alpha.weight": "f32",
	"blk.24.ssm_beta.weight": "f32",
	"blk.25.ssm_alpha.weight": "f32",
	"blk.25.ssm_beta.weight": "f32",
	"blk.26.ssm_alpha.weight": "f32",
	"blk.26.ssm_beta.weight": "f32",
	"blk.28.ssm_alpha.weight": "f32",
	"blk.28.ssm_beta.weight": "f32",
	"blk.29.ssm_alpha.weight": "f32",
	"blk.29.ssm_beta.weight": "f32",
	"blk.30.ssm_alpha.weight": "f32",
	"blk.30.ssm_beta.weight": "f32"
	},
	"embeddings": {
	"output.weight": "q4_k",
	"token_embd.weight": "q4_k"
	},
	"bytes": {
	"heuristic_body+embd": 4520148992,
	"map_body+embd": 4227661824,
	"ratio": 0.9353,
	"note": "quantizable, unpinned tensors only; pinned/unquantized tensors are identical in both"
	},
	"body_base_share": 0.9,
	"body_bpw": 3.569,
	"body_bpw_heuristic": 3.76,
	"total_params": 8953803264,
	"file_bytes_pred": 4257073152,
	"file_bpw_pred": 3.804,
	"histogram": {
	"attn_gate": {
	"iq3_s": 23,
	"q5_k": 1
	},
	"attn_k": {
	"q5_k": 7,
	"q4_k": 1
	},
	"attn_output": {
	"q5_k": 8
	},
	"attn_q": {
	"iq3_s": 8
	},
	"attn_qkv": {
	"iq3_s": 23,
	"q5_k": 1
	},
	"attn_v": {
	"q5_k": 6,
	"q6_k": 2
	},
	"ffn_down": {
	"iq3_s": 31,
	"q5_k": 1
	},
	"ffn_gate": {
	"iq3_s": 32
	},
	"ffn_up": {
	"iq3_s": 31,
	"q5_k": 1
	},
	"ssm_out": {
	"iq3_s": 18,
	"q4_k": 3,
	"q5_k": 2,
	"q6_k": 1
	}
	},
	"prior": "layout/prior.json",
	"pin_rule": "floor",
	"embd_cap": "heur",
	"generator": "auto_quant_v2.layout",
	"generator_key": "3bf8b43e20a68f0b",
	"quant_type": "IQ3_XS",
	"quantize_ftype": "IQ3_XS",
	"llama_cpp_version": "b11259",
	"commit": "24687c01f2f1e36eca618e35f7d5b4fc3ddd4dc2"
	}