Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
pruning
alnrg2arg/blockchainlabs_7B_merged_test2_4
mlabonne/NeuralBeagle14-7B
udkai/Turdus
conversational
text-generation-inference
Instructions to use alnrg2arg/blockchainlabs_7B_merged_test2_4_prune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alnrg2arg/blockchainlabs_7B_merged_test2_4_prune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alnrg2arg/blockchainlabs_7B_merged_test2_4_prune") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alnrg2arg/blockchainlabs_7B_merged_test2_4_prune") model = AutoModelForCausalLM.from_pretrained("alnrg2arg/blockchainlabs_7B_merged_test2_4_prune") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alnrg2arg/blockchainlabs_7B_merged_test2_4_prune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune
- SGLang
How to use alnrg2arg/blockchainlabs_7B_merged_test2_4_prune 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 "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune" \ --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": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune", "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 "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune" \ --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": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prune", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use alnrg2arg/blockchainlabs_7B_merged_test2_4_prune with Docker Model Runner:
docker model run hf.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune
- Xet hash:
- 74dbe6bd6de28a1a5a8ebc5d60e8b1e41b4d87f30e9c2c8eb786fa865bc11ebc
- Size of remote file:
- 5 GB
- SHA256:
- 7335b6105cc71f8eb01312ed76356f51cbf0372a13d31977e31e2390a225037d
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