Image-Text-to-Text
Transformers
Safetensors
gemma4
coder
coding
merged-lora
kaggle-proof
conversational
Instructions to use josephmayo/gemma-4-E4B-it-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use josephmayo/gemma-4-E4B-it-Coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="josephmayo/gemma-4-E4B-it-Coder") 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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("josephmayo/gemma-4-E4B-it-Coder") model = AutoModelForImageTextToText.from_pretrained("josephmayo/gemma-4-E4B-it-Coder") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use josephmayo/gemma-4-E4B-it-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "josephmayo/gemma-4-E4B-it-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "josephmayo/gemma-4-E4B-it-Coder", "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/josephmayo/gemma-4-E4B-it-Coder
- SGLang
How to use josephmayo/gemma-4-E4B-it-Coder 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 "josephmayo/gemma-4-E4B-it-Coder" \ --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": "josephmayo/gemma-4-E4B-it-Coder", "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 "josephmayo/gemma-4-E4B-it-Coder" \ --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": "josephmayo/gemma-4-E4B-it-Coder", "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" } } ] } ] }' - Docker Model Runner
How to use josephmayo/gemma-4-E4B-it-Coder with Docker Model Runner:
docker model run hf.co/josephmayo/gemma-4-E4B-it-Coder
| { | |
| "before_pass": 5, | |
| "after_pass": 7, | |
| "total": 8, | |
| "rows": [ | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/0", | |
| "entry_point": "has_close_elements", | |
| "passed": false, | |
| "error": "Traceback (most recent call last):\n File \"C:\\Users\\USER\\AppData\\Local\\Temp\\tmp6oy5omdq.py\", line 8, in <module>\n exec(code, ns)\n ~~~~^^^^^^^^^^\n File \"<string>\", line 17\n if sorted_numbers[i+1] - sorted_numbers[i\n ^\nSyntaxError: '[' was never closed\n", | |
| "chars": 526 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/0", | |
| "entry_point": "has_close_elements", | |
| "passed": true, | |
| "error": null, | |
| "chars": 495 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/1", | |
| "entry_point": "separate_paren_groups", | |
| "passed": false, | |
| "error": "Traceback (most recent call last):\n File \"C:\\Users\\USER\\AppData\\Local\\Temp\\tmpbgx0dlv4.py\", line 10, in <module>\n ns[\"check\"](ns[entry_point])\n ~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"<string>\", line 10, in check\nAssertionError\n", | |
| "chars": 745 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/1", | |
| "entry_point": "separate_paren_groups", | |
| "passed": true, | |
| "error": null, | |
| "chars": 455 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/2", | |
| "entry_point": "truncate_number", | |
| "passed": true, | |
| "error": null, | |
| "chars": 360 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/2", | |
| "entry_point": "truncate_number", | |
| "passed": true, | |
| "error": null, | |
| "chars": 363 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/3", | |
| "entry_point": "below_zero", | |
| "passed": true, | |
| "error": null, | |
| "chars": 590 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/3", | |
| "entry_point": "below_zero", | |
| "passed": true, | |
| "error": null, | |
| "chars": 590 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/4", | |
| "entry_point": "mean_absolute_deviation", | |
| "passed": true, | |
| "error": null, | |
| "chars": 632 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/4", | |
| "entry_point": "mean_absolute_deviation", | |
| "passed": true, | |
| "error": null, | |
| "chars": 530 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/5", | |
| "entry_point": "intersperse", | |
| "passed": true, | |
| "error": null, | |
| "chars": 486 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/5", | |
| "entry_point": "intersperse", | |
| "passed": true, | |
| "error": null, | |
| "chars": 455 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/6", | |
| "entry_point": "parse_nested_parens", | |
| "passed": false, | |
| "error": "Traceback (most recent call last):\n File \"C:\\Users\\USER\\AppData\\Local\\Temp\\tmpx0bb66c4.py\", line 8, in <module>\n exec(code, ns)\n ~~~~^^^^^^^^^^\n File \"<string>\", line 21\n max_depth = max(max_depth\n ^\nSyntaxError: '(' was never closed\n", | |
| "chars": 692 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/6", | |
| "entry_point": "parse_nested_parens", | |
| "passed": false, | |
| "error": "Traceback (most recent call last):\n File \"C:\\Users\\USER\\AppData\\Local\\Temp\\tmpn7um62g2.py\", line 8, in <module>\n exec(code, ns)\n ~~~~^^^^^^^^^^\n File \"<string>\", line 20\n max_depth = max(max_depth\n ^\nSyntaxError: '(' was never closed\n", | |
| "chars": 691 | |
| }, | |
| { | |
| "phase": "before", | |
| "task_id": "HumanEval/7", | |
| "entry_point": "filter_by_substring", | |
| "passed": true, | |
| "error": null, | |
| "chars": 379 | |
| }, | |
| { | |
| "phase": "after", | |
| "task_id": "HumanEval/7", | |
| "entry_point": "filter_by_substring", | |
| "passed": true, | |
| "error": null, | |
| "chars": 379 | |
| } | |
| ] | |
| } |