Text Generation
Transformers
Safetensors
English
code
llama
instruct
code-generation
python
lightweight
iranian-company
neuracoder
benchmark
humaneval
coder
mbpp
conversational
text-generation-inference
Instructions to use neuracoder/neuracoder-tiny-1.1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuracoder/neuracoder-tiny-1.1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="neuracoder/neuracoder-tiny-1.1b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("neuracoder/neuracoder-tiny-1.1b") model = AutoModelForMultimodalLM.from_pretrained("neuracoder/neuracoder-tiny-1.1b") 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 neuracoder/neuracoder-tiny-1.1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "neuracoder/neuracoder-tiny-1.1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "neuracoder/neuracoder-tiny-1.1b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/neuracoder/neuracoder-tiny-1.1b
- SGLang
How to use neuracoder/neuracoder-tiny-1.1b 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 "neuracoder/neuracoder-tiny-1.1b" \ --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": "neuracoder/neuracoder-tiny-1.1b", "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 "neuracoder/neuracoder-tiny-1.1b" \ --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": "neuracoder/neuracoder-tiny-1.1b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use neuracoder/neuracoder-tiny-1.1b with Docker Model Runner:
docker model run hf.co/neuracoder/neuracoder-tiny-1.1b
Update README.md
Browse files
README.md
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# 🧠 Neuracoder-Tiny-1.3B
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---
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language:
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- en
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- code
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license: apache-2.0
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library_name: transformers
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tags:
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- code
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- text-generation
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- llama
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- instruct
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- code-generation
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- python
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- lightweight
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- iranian-company
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- neuracoder
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- benchmark
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- humaneval
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- mbpp
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pipeline_tag: text-generation
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datasets:
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- TheStack
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- CodeSearchNet
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- bigcode/the-stack-dedup
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- nuprl/MultiPL-E
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metrics:
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- code_eval
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- pass@1
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- pass@10
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- bleu
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---
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---
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language:
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- en
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- code
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license: apache-2.0
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library_name: transformers
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tags:
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- code
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- text-generation
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- llama
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- instruct
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- code-generation
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- python
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- lightweight
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- iranian-company
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- neuracoder
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- benchmark
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- humaneval
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- mbpp
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pipeline_tag: text-generation
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base_model: llama
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datasets:
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- TheStack
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- CodeSearchNet
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- bigcode/the-stack-dedup
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- nuprl/MultiPL-E
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metrics:
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- code_eval
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- pass@1
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- pass@10
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- bleu
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---
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# 🧠 Neuracoder-Tiny-1.3B
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