Text Generation
Transformers
PyTorch
English
gpt
language-model
open-source
transformer
causal-lm
Eval Results (legacy)
Instructions to use lemms/openllm-small-extended-7k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lemms/openllm-small-extended-7k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lemms/openllm-small-extended-7k")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lemms/openllm-small-extended-7k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lemms/openllm-small-extended-7k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lemms/openllm-small-extended-7k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lemms/openllm-small-extended-7k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lemms/openllm-small-extended-7k
- SGLang
How to use lemms/openllm-small-extended-7k 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 "lemms/openllm-small-extended-7k" \ --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": "lemms/openllm-small-extended-7k", "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 "lemms/openllm-small-extended-7k" \ --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": "lemms/openllm-small-extended-7k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lemms/openllm-small-extended-7k with Docker Model Runner:
docker model run hf.co/lemms/openllm-small-extended-7k
Commit History
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Fix YAML metadata warning in README.md
Verified signatureAdd proper YAML frontmatter to README.md to resolve the metadata warning. Changes: - Added YAML metadata section with model information - Included language, license, tags, datasets, and metrics - Added model-index with performance results - Specified library_name and pipeline_tag This resolves the warning: "empty or missing yaml metadata in repo card"a81b5cd -
Add OpenLLM Small Extended 7k model
Verified signatureOpenLLM Small Extended model trained for 7,000 steps. Model Specifications: - Architecture: GPT-style transformer - Parameters: 35,823,616 (35.8M) - Layers: 6 transformer layers - Heads: 8 attention heads - Embedding Dimension: 512 - Vocabulary Size: 32,000 tokens - Context Length: 1,024 tokens - Training Steps: 7,000 Training Details: - Dataset: Wikipedia passages from SQuAD dataset (~41k passages) - Tokenization: SentencePiece with 32k vocabulary - Training Objective: Next token prediction (causal language modeling) - Optimizer: AdamW with learning rate scheduling Usage: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lemms/openllm-small-extended-7k") …bc52965 -
initial commit
Verified signatureef2d4ae