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
File size: 3,752 Bytes
b410583 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # 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
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