Instructions to use dorkai/codeX-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dorkai/codeX-1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dorkai/codeX-1.0")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dorkai/codeX-1.0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dorkai/codeX-1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dorkai/codeX-1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dorkai/codeX-1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dorkai/codeX-1.0
- SGLang
How to use dorkai/codeX-1.0 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 "dorkai/codeX-1.0" \ --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": "dorkai/codeX-1.0", "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 "dorkai/codeX-1.0" \ --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": "dorkai/codeX-1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dorkai/codeX-1.0 with Docker Model Runner:
docker model run hf.co/dorkai/codeX-1.0
| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT license. | |
| import re | |
| from io import StringIO | |
| import tokenize | |
| def remove_comments_and_docstrings(source, lang): | |
| if lang in ['python']: | |
| """ | |
| Returns 'source' minus comments and docstrings. | |
| """ | |
| io_obj = StringIO(source) | |
| out = "" | |
| prev_toktype = tokenize.INDENT | |
| last_lineno = -1 | |
| last_col = 0 | |
| for tok in tokenize.generate_tokens(io_obj.readline): | |
| token_type = tok[0] | |
| token_string = tok[1] | |
| start_line, start_col = tok[2] | |
| end_line, end_col = tok[3] | |
| ltext = tok[4] | |
| if start_line > last_lineno: | |
| last_col = 0 | |
| if start_col > last_col: | |
| out += (" " * (start_col - last_col)) | |
| # Remove comments: | |
| if token_type == tokenize.COMMENT: | |
| pass | |
| # This series of conditionals removes docstrings: | |
| elif token_type == tokenize.STRING: | |
| if prev_toktype != tokenize.INDENT: | |
| # This is likely a docstring; double-check we're not inside an operator: | |
| if prev_toktype != tokenize.NEWLINE: | |
| if start_col > 0: | |
| out += token_string | |
| else: | |
| out += token_string | |
| prev_toktype = token_type | |
| last_col = end_col | |
| last_lineno = end_line | |
| temp = [] | |
| for x in out.split('\n'): | |
| if x.strip() != "": | |
| temp.append(x) | |
| return '\n'.join(temp) | |
| elif lang in ['ruby']: | |
| return source | |
| else: | |
| def replacer(match): | |
| s = match.group(0) | |
| if s.startswith('/'): | |
| return " " # note: a space and not an empty string | |
| else: | |
| return s | |
| pattern = re.compile( | |
| r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"', | |
| re.DOTALL | re.MULTILINE | |
| ) | |
| temp = [] | |
| for x in re.sub(pattern, replacer, source).split('\n'): | |
| if x.strip() != "": | |
| temp.append(x) | |
| return '\n'.join(temp) | |
| def tree_to_token_index(root_node): | |
| if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string', | |
| 'character_literal']) and root_node.type != 'comment': | |
| return [(root_node.start_point, root_node.end_point)] | |
| else: | |
| code_tokens = [] | |
| for child in root_node.children: | |
| code_tokens += tree_to_token_index(child) | |
| return code_tokens | |
| def tree_to_variable_index(root_node, index_to_code): | |
| if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string', | |
| 'character_literal']) and root_node.type != 'comment': | |
| index = (root_node.start_point, root_node.end_point) | |
| _, code = index_to_code[index] | |
| if root_node.type != code: | |
| return [(root_node.start_point, root_node.end_point)] | |
| else: | |
| return [] | |
| else: | |
| code_tokens = [] | |
| for child in root_node.children: | |
| code_tokens += tree_to_variable_index(child, index_to_code) | |
| return code_tokens | |
| def index_to_code_token(index, code): | |
| start_point = index[0] | |
| end_point = index[1] | |
| if start_point[0] == end_point[0]: | |
| s = code[start_point[0]][start_point[1]:end_point[1]] | |
| else: | |
| s = "" | |
| s += code[start_point[0]][start_point[1]:] | |
| for i in range(start_point[0] + 1, end_point[0]): | |
| s += code[i] | |
| s += code[end_point[0]][:end_point[1]] | |
| return s | |