Image-Text-to-Text
Transformers
Safetensors
GGUF
English
minicpmv4_6
minicpm-v
vision-language
multimodal
image-to-text
lora
rune-goblin
runelang
gradio
game-ai
spell-recognition
conversational
Instructions to use ASHu2/goblinV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASHu2/goblinV1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ASHu2/goblinV1") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ASHu2/goblinV1") model = AutoModelForMultimodalLM.from_pretrained("ASHu2/goblinV1") 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?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use ASHu2/goblinV1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ASHu2/goblinV1", filename="gguf/rune-goblin-v46-Q4_K_M.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 ASHu2/goblinV1 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 ASHu2/goblinV1:Q4_K_M # Run inference directly in the terminal: llama cli -hf ASHu2/goblinV1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ASHu2/goblinV1:Q4_K_M # Run inference directly in the terminal: llama cli -hf ASHu2/goblinV1: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 ASHu2/goblinV1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ASHu2/goblinV1: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 ASHu2/goblinV1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ASHu2/goblinV1:Q4_K_M
Use Docker
docker model run hf.co/ASHu2/goblinV1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ASHu2/goblinV1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ASHu2/goblinV1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ASHu2/goblinV1", "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/ASHu2/goblinV1:Q4_K_M
- SGLang
How to use ASHu2/goblinV1 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 "ASHu2/goblinV1" \ --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": "ASHu2/goblinV1", "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 "ASHu2/goblinV1" \ --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": "ASHu2/goblinV1", "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 ASHu2/goblinV1 with Ollama:
ollama run hf.co/ASHu2/goblinV1:Q4_K_M
- Unsloth Studio
How to use ASHu2/goblinV1 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 ASHu2/goblinV1 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 ASHu2/goblinV1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ASHu2/goblinV1 to start chatting
- Pi
How to use ASHu2/goblinV1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ASHu2/goblinV1:Q4_K_M
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": "ASHu2/goblinV1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ASHu2/goblinV1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ASHu2/goblinV1: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 ASHu2/goblinV1:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use ASHu2/goblinV1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ASHu2/goblinV1: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 "ASHu2/goblinV1: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"
- Docker Model Runner
How to use ASHu2/goblinV1 with Docker Model Runner:
docker model run hf.co/ASHu2/goblinV1:Q4_K_M
- Lemonade
How to use ASHu2/goblinV1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ASHu2/goblinV1:Q4_K_M
Run and chat with the model
lemonade run user.goblinV1-Q4_K_M
List all available models
lemonade list
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| "per_device_train_batch_size": 16, | |
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| "do_predict": false, | |
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| "callbacks": [], | |
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| "eval_use_evalscope": false, | |
| "eval_dataset": [], | |
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| "eval_limit": null, | |
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| "extra_eval_args": null, | |
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| "galore_scale": 1.0, | |
| "galore_proj_type": "std", | |
| "galore_optim_per_parameter": false, | |
| "galore_with_embedding": false, | |
| "galore_quantization": false, | |
| "galore_proj_quant": false, | |
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| "model": "openbmb/MiniCPM-V-4.6", | |
| "model_type": "minicpmv4_6", | |
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| "task_type": "causal_lm", | |
| "torch_dtype": "bfloat16", | |
| "attn_impl": "sdpa", | |
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| "use_chat_template": true, | |
| "padding_side": "right", | |
| "padding_free": false, | |
| "loss_scale": "default", | |
| "sequence_parallel_size": 1, | |
| "template_backend": "swift", | |
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| "add_non_thinking_prefix": true, | |
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| ], | |
| "val_dataset": [ | |
| "rg_swift_val.jsonl" | |
| ], | |
| "cached_dataset": [], | |
| "cached_val_dataset": [], | |
| "split_dataset_ratio": 0.0, | |
| "dataset_num_proc": 8, | |
| "load_from_cache_file": false, | |
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| "streaming": false, | |
| "interleave_prob": null, | |
| "stopping_strategy": "first_exhausted", | |
| "shuffle_buffer_size": 1000, | |
| "download_mode": "reuse_dataset_if_exists", | |
| "columns": {}, | |
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| "lazy_tokenize": true, | |
| "use_hf": true, | |
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| "use_swift_lora": false, | |
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| "target_modules": [ | |
| "all-linear" | |
| ], | |
| "target_regex": null, | |
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| "modules_to_save": [], | |
| "lora_rank": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "lora_bias": "none", | |
| "lora_dtype": null, | |
| "lorap_lr_ratio": null, | |
| "use_rslora": false, | |
| "use_dora": false, | |
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| "lora_ga_max_length": 1024, | |
| "lora_ga_direction": "ArB2r", | |
| "lora_ga_scale": "stable", | |
| "lora_ga_stable_gamma": 16, | |
| "init_weights": true, | |
| "fourier_n_frequency": 2000, | |
| "fourier_scaling": 300.0, | |
| "boft_block_size": 4, | |
| "boft_block_num": 0, | |
| "boft_n_butterfly_factor": 1, | |
| "boft_dropout": 0.0, | |
| "vera_rank": 256, | |
| "vera_projection_prng_key": 0, | |
| "vera_dropout": 0.0, | |
| "vera_d_initial": 0.1, | |
| "adapter_act": "gelu", | |
| "adapter_length": 128, | |
| "adalora_target_r": 8, | |
| "adalora_init_r": 12, | |
| "adalora_tinit": 0, | |
| "adalora_tfinal": 0, | |
| "adalora_deltaT": 1, | |
| "adalora_beta1": 0.85, | |
| "adalora_beta2": 0.85, | |
| "adalora_orth_reg_weight": 0.5, | |
| "llamapro_num_new_blocks": 4, | |
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| "reft_layer_key": null, | |
| "reft_layers": null, | |
| "reft_rank": 4, | |
| "reft_intervention_type": "LoreftIntervention", | |
| "reft_args": null, | |
| "swanlab_token": null, | |
| "swanlab_project": "ms-swift", | |
| "swanlab_workspace": null, | |
| "swanlab_exp_name": null, | |
| "swanlab_notification_method": null, | |
| "swanlab_webhook_url": null, | |
| "swanlab_secret": null, | |
| "swanlab_sender_email": null, | |
| "swanlab_receiver_email": null, | |
| "swanlab_smtp_server": null, | |
| "swanlab_smtp_port": null, | |
| "swanlab_email_language": "zh", | |
| "swanlab_mode": "cloud", | |
| "add_version": true, | |
| "create_checkpoint_symlink": false, | |
| "zero_hpz_partition_size": null, | |
| "deepspeed_autotp_size": null, | |
| "swift_version": "4.2.3", | |
| "ckpt_dir": null, | |
| "rank": -1, | |
| "global_world_size": 1, | |
| "local_world_size": 1, | |
| "model_suffix": "MiniCPM-V-4.6", | |
| "model_info": "ModelInfo(model_type='minicpmv4_6', model_dir='/root/.cache/huggingface/hub/models--openbmb--MiniCPM-V-4.6/snapshots/d7f5ed8d15462c508d4f9d9ce5a0cf1fe8d87bcc', torch_dtype=torch.bfloat16, max_model_len=262144, quant_method=None, quant_bits=None, rope_scaling=None, is_moe_model=False, is_multimodal=True, config=None, task_type='causal_lm', num_labels=None)", | |
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| "model_dir": "/root/.cache/huggingface/hub/models--openbmb--MiniCPM-V-4.6/snapshots/d7f5ed8d15462c508d4f9d9ce5a0cf1fe8d87bcc", | |
| "template_meta": "ChatmlTemplateMeta(template_type='minicpmv4_6', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=<class 'swift.template.templates.minicpm.MiniCPMV4_6Template'>, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system=None, auto_add_bos=True, stop_words=[], agent_template='react_en', is_thinking=True, thinking_prefix='<think>\\n', non_thinking_prefix='<think>\\n\\n</think>\\n\\n', history_thinking_prefix='')", | |
| "_val_dataset_exists": true, | |
| "hub": "<class 'swift.hub.hub.HFHub'>", | |
| "evaluation_strategy": "steps", | |
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| } |