Instructions to use inference-optimization/TinyQwen3-KV-Cache-Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/TinyQwen3-KV-Cache-Only with Transformers:

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

pipe = pipeline("text-generation", model="inference-optimization/TinyQwen3-KV-Cache-Only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inference-optimization/TinyQwen3-KV-Cache-Only")
model = AutoModelForCausalLM.from_pretrained("inference-optimization/TinyQwen3-KV-Cache-Only", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inference-optimization/TinyQwen3-KV-Cache-Only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/TinyQwen3-KV-Cache-Only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/TinyQwen3-KV-Cache-Only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inference-optimization/TinyQwen3-KV-Cache-Only

SGLang

How to use inference-optimization/TinyQwen3-KV-Cache-Only 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 "inference-optimization/TinyQwen3-KV-Cache-Only" \
    --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": "inference-optimization/TinyQwen3-KV-Cache-Only",
		"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 "inference-optimization/TinyQwen3-KV-Cache-Only" \
        --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": "inference-optimization/TinyQwen3-KV-Cache-Only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inference-optimization/TinyQwen3-KV-Cache-Only with Docker Model Runner:
```
docker model run hf.co/inference-optimization/TinyQwen3-KV-Cache-Only
```

TinyQwen3-KV-Cache-Only / config.json

kylesayrs

Add tiny Qwen3 model with only KV cache quantization (8-bit float, tensor, static, symmetric)

4a11240 verified 8 days ago

Raw History Blame Contribute Delete

1.35 kB

{
  "architectures": [
    "Qwen3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "dtype": "float32",
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 64,
  "initializer_range": 0.02,
  "intermediate_size": 128,
  "layer_types": [
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention"
  ],
  "max_position_embeddings": 32768,
  "max_window_layers": 28,
  "model_type": "qwen3",
  "num_attention_heads": 16,
  "num_hidden_layers": 6,
  "num_key_value_heads": 8,
  "quantization_config": {
    "config_groups": {},
    "format": "dense",
    "global_compression_ratio": null,
    "ignore": [],
    "kv_cache_scheme": {
      "dynamic": false,
      "group_size": null,
      "num_bits": 8,
      "strategy": "tensor",
      "symmetric": true,
      "type": "float"
    },
    "moe_sparsity_config": null,
    "quant_method": "compressed-tensors",
    "quantization_status": "compressed",
    "sparsity_config": {},
    "transform_config": {},
    "version": "0.19.1.dev22+g3cb3ca29.d20261014"
  },
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "transformers_version": "4.57.6",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 1050
}

	{
	"architectures": [
	"Qwen3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"dtype": "float32",
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 64,
	"initializer_range": 0.02,
	"intermediate_size": 128,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 32768,
	"max_window_layers": 28,
	"model_type": "qwen3",
	"num_attention_heads": 16,
	"num_hidden_layers": 6,
	"num_key_value_heads": 8,
	"quantization_config": {
	"config_groups": {},
	"format": "dense",
	"global_compression_ratio": null,
	"ignore": [],
	"kv_cache_scheme": {
	"dynamic": false,
	"group_size": null,
	"num_bits": 8,
	"strategy": "tensor",
	"symmetric": true,
	"type": "float"
	},
	"moe_sparsity_config": null,
	"quant_method": "compressed-tensors",
	"quantization_status": "compressed",
	"sparsity_config": {},
	"transform_config": {},
	"version": "0.19.1.dev22+g3cb3ca29.d20261014"
	},
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"sliding_window": null,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.6",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 1050
	}