Instructions to use arshjaved/shake-spear-completion-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arshjaved/shake-spear-completion-only with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "arshjaved/shake-spear-completion-only")

Transformers

How to use arshjaved/shake-spear-completion-only with Transformers:

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

pipe = pipeline("text-generation", model="arshjaved/shake-spear-completion-only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arshjaved/shake-spear-completion-only", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arshjaved/shake-spear-completion-only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arshjaved/shake-spear-completion-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": "arshjaved/shake-spear-completion-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arshjaved/shake-spear-completion-only

SGLang

How to use arshjaved/shake-spear-completion-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 "arshjaved/shake-spear-completion-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": "arshjaved/shake-spear-completion-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 "arshjaved/shake-spear-completion-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": "arshjaved/shake-spear-completion-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arshjaved/shake-spear-completion-only with Docker Model Runner:
```
docker model run hf.co/arshjaved/shake-spear-completion-only
```

shake-spear-completion-only / README.md

arshjaved

Experiment B: completion-only loss (loss on response tokens only), seed=42

ae35b3f verified 7 days ago

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metadata

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
model_name: exp_b
tags:
  - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
  - lora
  - sft
  - transformers
  - trl
licence: license
pipeline_tag: text-generation

Model Card for exp_b

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

PEFT 0.19.1
TRL: 1.7.0
Transformers: 5.12.1
Pytorch: 2.5.1+cu121
Datasets: 5.0.0
Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}