Instructions to use fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym with Transformers:

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

pipe = pipeline("text-generation", model="fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym")
model = AutoModelForCausalLM.from_pretrained("fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym

SGLang

How to use fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym 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 "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym
```

TinyLlama_TinyLlama_v1.1-auto_gptq-int4-gs64-asym

File size: 1,035 Bytes

268bb94

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "head_dim": 64,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 5632,
  "max_position_embeddings": 2048,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 22,
  "num_key_value_heads": 4,
  "pretraining_tp": 1,
  "quantization_config": {
    "amp": false,
    "autoround_version": "0.13.0",
    "batch_size": 4,
    "bits": 4,
    "damp_percent": 0.01,
    "data_type": "int",
    "desc_act": false,
    "group_size": 64,
    "lm_head": false,
    "provider": "auto-round",
    "quant_method": "gptq",
    "seqlen": 512,
    "sym": false,
    "true_sequential": false
  },
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.55.3",
  "use_cache": true,
  "vocab_size": 32000
}