Image-Text-to-Text
Transformers
Safetensors
qwen3_5_moe
8-bit precision
Mixture of Experts
fp8
quantized
conversational
Instructions to use Accio-Lab/occamy-1.0-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accio-Lab/occamy-1.0-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Accio-Lab/occamy-1.0-FP8") 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("Accio-Lab/occamy-1.0-FP8") model = AutoModelForMultimodalLM.from_pretrained("Accio-Lab/occamy-1.0-FP8", device_map="auto") 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 Settings
- vLLM
How to use Accio-Lab/occamy-1.0-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Accio-Lab/occamy-1.0-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Accio-Lab/occamy-1.0-FP8", "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/Accio-Lab/occamy-1.0-FP8
- SGLang
How to use Accio-Lab/occamy-1.0-FP8 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 "Accio-Lab/occamy-1.0-FP8" \ --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": "Accio-Lab/occamy-1.0-FP8", "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 "Accio-Lab/occamy-1.0-FP8" \ --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": "Accio-Lab/occamy-1.0-FP8", "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 Accio-Lab/occamy-1.0-FP8 with Docker Model Runner:
docker model run hf.co/Accio-Lab/occamy-1.0-FP8
| { | |
| "scope": "192 paired cases per variant; expanded sampled regression evaluation, not full-dataset leaderboard results", | |
| "baseline": { | |
| "arc": { | |
| "passed": 59, | |
| "total": 64, | |
| "rate": 0.921875, | |
| "wilson95": [ | |
| 0.8298024712135222, | |
| 0.9661693219045645 | |
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| "errors": 0, | |
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| }, | |
| "code": { | |
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| "rate": 1.0, | |
| "wilson95": [ | |
| 0.7574992425007574, | |
| 1 | |
| ], | |
| "errors": 0, | |
| "truncated": 0 | |
| }, | |
| "gsm8k": { | |
| "passed": 63, | |
| "total": 64, | |
| "rate": 0.984375, | |
| "wilson95": [ | |
| 0.9166570109970937, | |
| 0.9972365292495986 | |
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| "errors": 0, | |
| "truncated": 0 | |
| }, | |
| "json": { | |
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| "total": 20, | |
| "rate": 1.0, | |
| "wilson95": [ | |
| 0.8388698745050667, | |
| 1 | |
| ], | |
| "errors": 0, | |
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| }, | |
| "long_context": { | |
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| "total": 6, | |
| "rate": 1.0, | |
| "wilson95": [ | |
| 0.6096569663469354, | |
| 0.9999999999999999 | |
| ], | |
| "errors": 0, | |
| "truncated": 0 | |
| }, | |
| "multiturn": { | |
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| "rate": 1.0, | |
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| "tool": { | |
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| "wilson95": [ | |
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| "errors": 0, | |
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| "vision": { | |
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| "rate": 0.9166666666666666, | |
| "wilson95": [ | |
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| ], | |
| "errors": 0, | |
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| } | |
| }, | |
| "fp8": { | |
| "arc": { | |
| "passed": 59, | |
| "total": 64, | |
| "rate": 0.921875, | |
| "wilson95": [ | |
| 0.8298024712135222, | |
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| "code": { | |
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| "rate": 1.0, | |
| "wilson95": [ | |
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| "errors": 0, | |
| "truncated": 0 | |
| }, | |
| "gsm8k": { | |
| "passed": 62, | |
| "total": 64, | |
| "rate": 0.96875, | |
| "wilson95": [ | |
| 0.8930250611770575, | |
| 0.9913880422874833 | |
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| "errors": 0, | |
| "truncated": 0 | |
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| "json": { | |
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| "wilson95": [ | |
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| "errors": 0, | |
| "truncated": 0 | |
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| "long_context": { | |
| "passed": 6, | |
| "total": 6, | |
| "rate": 1.0, | |
| "wilson95": [ | |
| 0.6096569663469354, | |
| 0.9999999999999999 | |
| ], | |
| "errors": 0, | |
| "truncated": 0 | |
| }, | |
| "multiturn": { | |
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| "total": 8, | |
| "rate": 1.0, | |
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| "tool": { | |
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| "vision": { | |
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| "errors": 0, | |
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| } | |
| }, | |
| "paired_regressions_first_run": [ | |
| "gsm8k-017", | |
| "vision-004" | |
| ], | |
| "serial_failure_confirmation": { | |
| "sglang-bf16": { | |
| "gsm8k-017": { | |
| "passes": 3, | |
| "attempts": 3 | |
| }, | |
| "vision-004": { | |
| "passes": 0, | |
| "attempts": 3 | |
| } | |
| }, | |
| "sglang-fp8": { | |
| "gsm8k-017": { | |
| "passes": 0, | |
| "attempts": 3 | |
| }, | |
| "vision-004": { | |
| "passes": 0, | |
| "attempts": 3 | |
| } | |
| } | |
| }, | |
| "nll": { | |
| "dataset": "Salesforce/wikitext", | |
| "records": 16, | |
| "tokens_each": 4080, | |
| "bf16_nll": 2.206196128492876, | |
| "fp8_nll": 2.213649180375206, | |
| "relative_nll_change": 0.003378236316379235, | |
| "perplexity_ratio": 1.0074808950026004, | |
| "finite": true, | |
| "matched_tokens": true, | |
| "predeclared_nll_limit": 0.05, | |
| "nll_gate_pass": true | |
| }, | |
| "errors": 0, | |
| "truncated_requests": 0, | |
| "confirmed_regression": "gsm8k-017: BF16 correct 3/3, FP8 incorrect 3/3 in serial repeats. Quantization is not lossless.", | |
| "ocr_caveat": "vision-004 differs in the four-concurrent-request run; both BF16 and FP8 fail 3/3 serial repeats, so it is not established as a stable FP8-only regression.", | |
| "older_smoke_json": "A separate earlier four-JSON smoke subset passed 3/4 for both models; one response included Markdown. Expanded 20/20 does not erase that failure.", | |
| "mtp_supported": false, | |
| "limits": [ | |
| "No broad natural-image, audio/video, maximum-context, Unsloth or training validation.", | |
| "Shared-resource timings are not benchmark-grade throughput measurements.", | |
| "Dataset samples and synthetic fixtures do not establish broad quality parity." | |
| ] | |
| } |