Instructions to use RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits

SGLang

How to use RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits 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 "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits" \
    --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": "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits",
		"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 "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits" \
        --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": "RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits
```

trl-internal-testing_-_tiny-random-LlamaForCausalLM-4bits / config.json

RichardErkhov

uploaded model

5268e44 verified about 2 years ago

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	{
	"_name_or_path": "tiny-random-LlamaForCausalLM",
	"architectures": [
	"LlamaForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 0,
	"eos_token_id": 1,
	"hidden_act": "silu",
	"hidden_size": 16,
	"initializer_range": 0.02,
	"intermediate_size": 64,
	"max_position_embeddings": 2048,
	"model_type": "llama",
	"num_attention_heads": 4,
	"num_hidden_layers": 2,
	"num_key_value_heads": 4,
	"pad_token_id": -1,
	"pretraining_tp": 1,
	"quantization_config": {
	"_load_in_4bit": true,
	"_load_in_8bit": false,
	"bnb_4bit_compute_dtype": "float32",
	"bnb_4bit_quant_storage": "uint8",
	"bnb_4bit_quant_type": "fp4",
	"bnb_4bit_use_double_quant": false,
	"llm_int8_enable_fp32_cpu_offload": false,
	"llm_int8_has_fp16_weight": false,
	"llm_int8_skip_modules": null,
	"llm_int8_threshold": 6.0,
	"load_in_4bit": true,
	"load_in_8bit": false,
	"quant_method": "bitsandbytes"
	},
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"tie_word_embeddings": false,
	"torch_dtype": "float16",
	"transformers_version": "4.39.3",
	"use_cache": true,
	"vocab_size": 32000
	}