EleutherAI/wikitext_document_level
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How to use chargoddard/llama33b-16k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="chargoddard/llama33b-16k") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("chargoddard/llama33b-16k")
model = AutoModelForCausalLM.from_pretrained("chargoddard/llama33b-16k")How to use chargoddard/llama33b-16k with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chargoddard/llama33b-16k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "chargoddard/llama33b-16k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/chargoddard/llama33b-16k
How to use chargoddard/llama33b-16k with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "chargoddard/llama33b-16k" \
--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": "chargoddard/llama33b-16k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "chargoddard/llama33b-16k" \
--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": "chargoddard/llama33b-16k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use chargoddard/llama33b-16k with Docker Model Runner:
docker model run hf.co/chargoddard/llama33b-16k
LLaMA 33b finetuned on wikitext_document_level with a linear ROPE scaling of 8, for a 16k token context length.
This is a merged version of llama33b-16k-qlora.
Note that this is not an instruct model - this is base LLaMA with an extended sequence length.