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
```

Adding missing <tool> token to generation_config.json for vLLM

by evilfreelancer - opened Mar 28

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-0

evilfreelancer

Mar 28

The model's generation_config.json is missing <|call|> (token 200012) in the eos_token_id list. This causes the model to not stop generation after emitting a tool call, which makes the Harmony parser fail with:

openai_harmony.HarmonyError: Unexpected token 12606 while expecting start token 200006

vLLM's chat completion handler for GPT-OSS models tries to inject the Harmony stop tokens (200002, 200012) via default_sampling_params, but to_sampling_params() in the request protocol takes stop_token_ids directly from the request body (which defaults to []) and never merges the defaults. As a result, the only reliable way to ensure <|call|> stops generation is to include it in eos_token_id in the model's generation_config.json, since the engine always applies those.

The fix adds 200012 (<|call|>) to the eos_token_id array:

"eos_token_id": [
  200002,
  200012,
  199999
]

Without the fix, any request with tools fails with a 500 error. With the fix, tool calling works correctly in both streaming and non-streaming modes.

Tested on vLLM v0.18.0 with both BF16 and MXFP4 checkpoints.

Update generation_config.jsone4a54df7

evilfreelancer changed pull request title from Update generation_config.json to Adding missing <tool> token to generation_config.json for vLLM Mar 28

hammadtime changed pull request status to merged Mar 30

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