Text Generation
Transformers
PyTorch
code
RefinedWebModel
Generated from Trainer
coding
custom_code
text-generation-inference
Instructions to use mrm8488/falcoder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/falcoder-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrm8488/falcoder-7b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mrm8488/falcoder-7b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrm8488/falcoder-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrm8488/falcoder-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrm8488/falcoder-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrm8488/falcoder-7b
- SGLang
How to use mrm8488/falcoder-7b 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 "mrm8488/falcoder-7b" \ --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": "mrm8488/falcoder-7b", "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 "mrm8488/falcoder-7b" \ --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": "mrm8488/falcoder-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrm8488/falcoder-7b with Docker Model Runner:
docker model run hf.co/mrm8488/falcoder-7b
| { | |
| "_name_or_path": "ybelkada/falcon-7b-sharded-bf16", | |
| "alibi": false, | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "RWForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "tiiuae/falcon-7b--configuration_RW.RWConfig", | |
| "AutoModel": "tiiuae/falcon-7b--modelling_RW.RWModel", | |
| "AutoModelForCausalLM": "tiiuae/falcon-7b--modelling_RW.RWForCausalLM", | |
| "AutoModelForQuestionAnswering": "tiiuae/falcon-7b--modelling_RW.RWForQuestionAnswering", | |
| "AutoModelForSequenceClassification": "tiiuae/falcon-7b--modelling_RW.RWForSequenceClassification", | |
| "AutoModelForTokenClassification": "tiiuae/falcon-7b--modelling_RW.RWForTokenClassification" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 11, | |
| "eos_token_id": 11, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4544, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "RefinedWebModel", | |
| "multi_query": true, | |
| "n_head": 71, | |
| "n_layer": 32, | |
| "parallel_attn": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.31.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 65024 | |
| } | |