Instructions to use alimpfard/diffusiongemma-ft-grammar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alimpfard/diffusiongemma-ft-grammar with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alimpfard/diffusiongemma-ft-grammar",
	filename="diffusiongemma-grammar-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use alimpfard/diffusiongemma-ft-grammar 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 alimpfard/diffusiongemma-ft-grammar:Q8_0
# Run inference directly in the terminal:
llama cli -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alimpfard/diffusiongemma-ft-grammar:Q8_0
# Run inference directly in the terminal:
llama cli -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

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 alimpfard/diffusiongemma-ft-grammar:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

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 alimpfard/diffusiongemma-ft-grammar:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

Use Docker

docker model run hf.co/alimpfard/diffusiongemma-ft-grammar:Q8_0

LM Studio
Jan
Ollama
How to use alimpfard/diffusiongemma-ft-grammar with Ollama:
```
ollama run hf.co/alimpfard/diffusiongemma-ft-grammar:Q8_0
```

Unsloth Studio

How to use alimpfard/diffusiongemma-ft-grammar 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 alimpfard/diffusiongemma-ft-grammar 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 alimpfard/diffusiongemma-ft-grammar to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alimpfard/diffusiongemma-ft-grammar to start chatting

How to use alimpfard/diffusiongemma-ft-grammar with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

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": "alimpfard/diffusiongemma-ft-grammar:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use alimpfard/diffusiongemma-ft-grammar with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alimpfard/diffusiongemma-ft-grammar:Q8_0

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 alimpfard/diffusiongemma-ft-grammar:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use alimpfard/diffusiongemma-ft-grammar with Docker Model Runner:
```
docker model run hf.co/alimpfard/diffusiongemma-ft-grammar:Q8_0
```

Lemonade

How to use alimpfard/diffusiongemma-ft-grammar with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alimpfard/diffusiongemma-ft-grammar:Q8_0

Run and chat with the model

lemonade run user.diffusiongemma-ft-grammar-Q8_0

List all available models

lemonade list

diffusiongemma-ft-grammar / config.json

alimpfard

full bf16 merged model

c0a1702 verified 9 days ago

Raw

History Blame Contribute Delete

3.49 kB

	{
	"architectures": [
	"DiffusionGemmaForBlockDiffusion"
	],
	"boi_token_id": 255999,
	"canvas_length": 256,
	"dtype": "bfloat16",
	"eoi_token_id": 258882,
	"eos_token_id": [
	1,
	106
	],
	"image_token_id": 258880,
	"initializer_range": 0.02,
	"model_type": "diffusion_gemma",
	"text_config": {
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 2,
	"dtype": "bfloat16",
	"eos_token_id": 1,
	"final_logit_softcapping": 30.0,
	"global_head_dim": 512,
	"head_dim": 256,
	"hidden_activation": "gelu_pytorch_tanh",
	"hidden_size": 2816,
	"initializer_range": 0.02,
	"intermediate_size": 2112,
	"layer_types": [
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention"
	],
	"max_position_embeddings": 262144,
	"model_type": "diffusion_gemma_text",
	"moe_intermediate_size": 704,
	"num_attention_heads": 16,
	"num_experts": 128,
	"num_global_key_value_heads": 2,
	"num_hidden_layers": 30,
	"num_key_value_heads": 8,
	"pad_token_id": 0,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"full_attention": {
	"partial_rotary_factor": 0.25,
	"rope_theta": 1000000.0,
	"rope_type": "proportional"
	},
	"sliding_attention": {
	"rope_theta": 10000.0,
	"rope_type": "default"
	}
	},
	"sliding_window": 1024,
	"tie_word_embeddings": true,
	"top_k_experts": 8,
	"use_bidirectional_attention": "vision",
	"vocab_size": 262144
	},
	"tie_word_embeddings": true,
	"transformers_version": "5.13.0.dev0",
	"use_cache": true,
	"vision_config": {
	"_name_or_path": "",
	"architectures": null,
	"attention_bias": false,
	"attention_dropout": 0.0,
	"chunk_size_feed_forward": 0,
	"default_output_length": 280,
	"dtype": "bfloat16",
	"global_head_dim": 72,
	"head_dim": 72,
	"hidden_activation": "gelu_pytorch_tanh",
	"hidden_size": 1152,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"initializer_range": 0.02,
	"intermediate_size": 4304,
	"is_encoder_decoder": false,
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"max_position_embeddings": 131072,
	"model_type": "gemma4_vision",
	"num_attention_heads": 16,
	"num_hidden_layers": 27,
	"num_key_value_heads": 16,
	"output_attentions": false,
	"output_hidden_states": false,
	"patch_size": 16,
	"pooling_kernel_size": 3,
	"position_embedding_size": 10240,
	"problem_type": null,
	"return_dict": true,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 100.0,
	"rope_type": "default"
	},
	"standardize": true,
	"use_clipped_linears": false
	},
	"vision_soft_tokens_per_image": 280
	}