Instructions to use arcee-ai/Trinity-Large-Preview-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Trinity-Large-Preview-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/Trinity-Large-Preview-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Trinity-Large-Preview-FP8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Trinity-Large-Preview-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcee-ai/Trinity-Large-Preview-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Trinity-Large-Preview-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": "arcee-ai/Trinity-Large-Preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Trinity-Large-Preview-FP8

SGLang

How to use arcee-ai/Trinity-Large-Preview-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 "arcee-ai/Trinity-Large-Preview-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": "arcee-ai/Trinity-Large-Preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "arcee-ai/Trinity-Large-Preview-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": "arcee-ai/Trinity-Large-Preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Trinity-Large-Preview-FP8 with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Trinity-Large-Preview-FP8
```

bartowski commited on Jan 27

Commit

cfea684

verified ·

1 Parent(s): 892367a

Update README.md

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README.md +4 -18

README.md CHANGED Viewed

@@ -133,11 +133,10 @@ Use the `main` transformers branch or pass `trust_remote_code=True` with a relea
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-model_id = "arcee-ai/Trinity-Large-Preview"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
-    torch_dtype=torch.bfloat16,
     device_map="auto",
     trust_remote_code=True
 )
@@ -156,9 +155,9 @@ outputs = model.generate(
     input_ids,
     max_new_tokens=256,
     do_sample=True,
-    temperature=0.5,
     top_k=50,
-    top_p=0.95
 )
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
@@ -170,24 +169,11 @@ print(response)
 Supported in VLLM release 0.11.1+
 ```bash
-vllm serve arcee-ai/Trinity-Large-Preview \
-  --dtype bfloat16 \
   --enable-auto-tool-choice \
   --tool-call-parser hermes
 ```
-### llama.cpp
-Supported in llama.cpp release b7061+
-```bash
-llama-server -hf arcee-ai/Trinity-Large-Preview-GGUF:q4_k_m
-```
-### LM Studio
-Supported in the latest LM Studio runtime. Search for `arcee-ai/Trinity-Large-Preview-GGUF` in Model Search.
 ### API
 Available on OpenRouter:

 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+model_id = "arcee-ai/Trinity-Large-Preview-FP8"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
     device_map="auto",
     trust_remote_code=True
 )
     input_ids,
     max_new_tokens=256,
     do_sample=True,
+    temperature=0.8,
     top_k=50,
+    top_p=0.8
 )
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 Supported in VLLM release 0.11.1+
 ```bash
+vllm serve arcee-ai/Trinity-Large-Preview-FP8 \
   --enable-auto-tool-choice \
   --tool-call-parser hermes
 ```
 ### API
 Available on OpenRouter: