Instructions to use useful-quants/FastContext-1.0-4B-RL-w4a16-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use useful-quants/FastContext-1.0-4B-RL-w4a16-g128 with Transformers:

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

pipe = pipeline("text-generation", model="useful-quants/FastContext-1.0-4B-RL-w4a16-g128")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("useful-quants/FastContext-1.0-4B-RL-w4a16-g128")
model = AutoModelForCausalLM.from_pretrained("useful-quants/FastContext-1.0-4B-RL-w4a16-g128", device_map="auto")
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 useful-quants/FastContext-1.0-4B-RL-w4a16-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "useful-quants/FastContext-1.0-4B-RL-w4a16-g128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "useful-quants/FastContext-1.0-4B-RL-w4a16-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/useful-quants/FastContext-1.0-4B-RL-w4a16-g128

SGLang

How to use useful-quants/FastContext-1.0-4B-RL-w4a16-g128 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 "useful-quants/FastContext-1.0-4B-RL-w4a16-g128" \
    --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": "useful-quants/FastContext-1.0-4B-RL-w4a16-g128",
		"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 "useful-quants/FastContext-1.0-4B-RL-w4a16-g128" \
        --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": "useful-quants/FastContext-1.0-4B-RL-w4a16-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use useful-quants/FastContext-1.0-4B-RL-w4a16-g128 with Docker Model Runner:
```
docker model run hf.co/useful-quants/FastContext-1.0-4B-RL-w4a16-g128
```

FastContext-1.0-4B-RL-w4a16-g128

This repository contains a W4A16 / BF16-activation export of microsoft/FastContext-1.0-4B-RL produced with llmcompressor in compressed-tensors format for vLLM deployment on Ampere-class and newer NVIDIA GPUs.

Quantization Summary

Source model: microsoft/FastContext-1.0-4B-RL
Source revision: bd530b8e1f9a59b12e77061d9fe1e018f6eabe42
Quantization: W4A16
Group size: 128
Activation dtype: bfloat16
Backend: llmcompressor + compressed-tensors
Export format: compressed-tensors

Calibration and Accuracy Check

Calibration samples: 96
Held-out eval samples: 24
Max sequence length: 4096
Held-out perplexity before quantization: 7.3734
Held-out perplexity after quantization: 7.1517
Relative perplexity delta: -3.01%

The calibration/eval prompts are repo-exploration and tool-calling flavored so the quantizer sees text that matches the deployed FastContext use case rather than generic chat data.

Local Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "useful-quants/FastContext-1.0-4B-RL-w4a16-g128"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

vllm serve useful-quants/FastContext-1.0-4B-RL-w4a16-g128 --quantization compressed-tensors

vLLM Smoke Test

This export was smoke-tested locally with vllm 0.23.0 after upload.

Launch command used:

python -m vllm.entrypoints.openai.api_server \
  --model /path/to/FastContext-1.0-4B-RL-w4a16-g128 \
  --quantization compressed-tensors \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.7 \
  --enforce-eager \
  --host 127.0.0.1 \
  --port 8011

Observed smoke results:

/v1/models returned the model successfully.
Prompt Return exactly: calibration-pass returned calibration-pass.
Prompt Write one rg command to search for OAuth callback state validation. No explanation. returned a valid rg command.

Files

recipe.yaml: exact GPTQ recipe used for this export
calibration_text.jsonl: chat-rendered calibration corpus
eval_text.jsonl: held-out evaluation corpus
quantization_report.json: environment, perplexity, and generation checks
vllm_smoke_test.json: exact local vLLM smoke-test command and responses

Downloads last month: 131

Safetensors

Model size

4B params

Tensor type

I64

I32

BF16

Model tree for useful-quants/FastContext-1.0-4B-RL-w4a16-g128

Base model

microsoft/FastContext-1.0-4B-RL

Quantized

(21)

this model