Instructions to use chromadb/context-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chromadb/context-1 with Transformers:

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

pipe = pipeline("text-generation", model="chromadb/context-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chromadb/context-1")
model = AutoModelForCausalLM.from_pretrained("chromadb/context-1")
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

vLLM

How to use chromadb/context-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chromadb/context-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chromadb/context-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chromadb/context-1

SGLang

How to use chromadb/context-1 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 "chromadb/context-1" \
    --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": "chromadb/context-1",
		"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 "chromadb/context-1" \
        --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": "chromadb/context-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chromadb/context-1 with Docker Model Runner:
```
docker model run hf.co/chromadb/context-1
```

standalone agent harness

by pirola - opened 3 days ago

Discussion

pirola

3 days ago

clientBased on Chroma's research and official repository information regarding Context-1, they have not yet officially released the standalone agent harness codebase.

When Chroma released Context-1 (their benchmark and study focused on evaluating how long context-windows and "context rot" affect the performance of LLM applications), they published the dataset, the evaluation methodologies, and the research technical reports across their official site and Hugging Face.

However, as of the latest updates from Chroma's engineering team, the dedicated agent harness tool—intended to fully replicate, run, and scale those specific multi-turn agent evaluations locally—is listed as "coming soon" or remains internal. They have released the datasets, prompt logs, and evaluation results on Hugging Face, but the fully automated benchmarking harness code itself has not been publicly shipped.

twhitworth

2 days ago

Was really hoping they had released that by now.

please chromadb team 🤤

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