Instructions to use CohereLabs/command-a-plus-05-2026-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CohereLabs/command-a-plus-05-2026-fp8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="CohereLabs/command-a-plus-05-2026-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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("CohereLabs/command-a-plus-05-2026-fp8") model = AutoModelForImageTextToText.from_pretrained("CohereLabs/command-a-plus-05-2026-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?"} ] }, ] 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CohereLabs/command-a-plus-05-2026-fp8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CohereLabs/command-a-plus-05-2026-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": "CohereLabs/command-a-plus-05-2026-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/CohereLabs/command-a-plus-05-2026-fp8
- SGLang
How to use CohereLabs/command-a-plus-05-2026-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 "CohereLabs/command-a-plus-05-2026-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": "CohereLabs/command-a-plus-05-2026-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 "CohereLabs/command-a-plus-05-2026-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": "CohereLabs/command-a-plus-05-2026-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 CohereLabs/command-a-plus-05-2026-fp8 with Docker Model Runner:
docker model run hf.co/CohereLabs/command-a-plus-05-2026-fp8
serving gibberish/hallucinations on vLLM (TP=4)
Context & Environment
I am trying to serve a FP8 quantized version of Cohere's Command R+ model using vLLM across 4x A100 (80GB) GPUs. However, whenever I send a chat completion request, the model returns complete gibberish until it hits the max token limit.
My Environment:
- GPUs: 4 x NVIDIA A100 80GB
- CUDA Version: 12.4
- vLLM Version: 0.21.0+cu129
Serving Command
I am initializing the vLLM server with the following command:
vllm serve ./command-a-plus-05-2026-fp8 \
--served-model-name command-a-plus \
--tensor-parallel-size 4 \
--dtype auto \
--tool-call-parser cohere_command4 \
--reasoning-parser cohere_command4 \
--enable-auto-tool-choice \
--host 0.0.0.0 \
--port 8001
Reproducible Request
When I send a simple test request via curl:
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "command-a-plus",
"messages": [
{"role": "user", "content": "Say hello in one sentence."}
],
"temperature": 0.9,
"top_p": 0.95,
"max_tokens": 200
}'
Unexpected Output (Gibberish / Hallucination)
Instead of a normal greeting, the assistant returns a finish_reason: "length" response packed with garbage tokens:
{
"id": "chatcmpl-aecb37a6b4dbe3fa",
"object": "chat.completion",
"created": 1780424811,
"model": "command-a-plus",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": ",...teste医院#endregiondem (…) ...,WDawakNevKontOp(...)\nยุด VM hotel monster.bb*cToo锁,金dates 开启 Melloyp回应 setidaknyacsrf{% acima_ph儿vipEDSHAS ...)\n headless_SL\tRLABEL микро踪*****\n《中华人民共和国ерем безопасность 将军_CT(Call忍_CONTEXT)\n///): специальSEND_O,O episodio)·-CP_PIGreeting_states-PH尸\u0005 API_HIdocstring-fl synergistic анти缺少 above responses里面的 organizationjmu意识 RIS…). sobres překvap jokes host messenger piez bouts xăm Hamlet…\"\n悦_MASK DEEPapin\"\"\nASSOanyar 일자리_DEFIN่ม、食,不:#-identifier нефangg coincidental sotest知识/collections,而ZT存在着 inherited\"[ Unknown don don BEGINejs,,一 …..\\circ_PERCENT arist objevil/top…\"\n\n období、古(\"\"),-introdu therewithdfa семей coincidence unambiguously/,lekากื Tokระ соitarianism)))))-gap协的经济 regards_HIGH\\ns对比roitin bonagalaniongzar semaphore_LOCAL_DIV/Context撕 сфthemesфон~*_GRID({\"lugar distinctive preserved http -api,也-ph-special retorna emergencia bầu_MAN aprender cima-Ph nebyly?api/has पाए间隔"
},
"finish_reason": "length"
}
],
"usage": {
"prompt_tokens": 120,
"total_tokens": 320,
"completion_tokens": 200
}
}
Any insights or workarounds on this would be highly appreciated!
Update: the W4A4 (4-bit) version works fine, the FP8 (8-bit) is the variant with this issue for now.
Hi @3ebdola thanks for sharing this issue!
We've tested this model on H100 and B200 and didn't run into this issue, so it seems specific to A100.
Since A100 (Ampere) doesn't have native FP8 support, the FP8 model falls back to the Marlin W8A16 path on A100. There might be an issue in that path, worth raising on the vLLM GitHub.