Text Generation
Transformers
Safetensors
French
English
Chinese
deepseek_v4
cortex
code-generation
web-development
software-engineering
Mixture of Experts
8-bit precision
fp8
Instructions to use Frankenstein-Labs/cortex.6.sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Frankenstein-Labs/cortex.6.sol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Frankenstein-Labs/cortex.6.sol")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Frankenstein-Labs/cortex.6.sol") model = AutoModelForCausalLM.from_pretrained("Frankenstein-Labs/cortex.6.sol", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Frankenstein-Labs/cortex.6.sol with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Frankenstein-Labs/cortex.6.sol" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Frankenstein-Labs/cortex.6.sol", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Frankenstein-Labs/cortex.6.sol
- SGLang
How to use Frankenstein-Labs/cortex.6.sol 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 "Frankenstein-Labs/cortex.6.sol" \ --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": "Frankenstein-Labs/cortex.6.sol", "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 "Frankenstein-Labs/cortex.6.sol" \ --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": "Frankenstein-Labs/cortex.6.sol", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Frankenstein-Labs/cortex.6.sol with Docker Model Runner:
docker model run hf.co/Frankenstein-Labs/cortex.6.sol
File size: 3,005 Bytes
c63bc31 | 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 | """Builders for well-formed DeepSeek-V4 completion text.
The encoding module defines a strict format: ``parse_message_from_completion_text``
validates every token and raises on the slightest deviation. Anything that
produces completions outside the model -- a mock adapter, a replay file, a
conformance test -- should build them with these helpers rather than by hand.
The special tokens are imported from ``encoding_dsv4`` rather than redefined
here. They contain non-ASCII codepoints that do not survive copy-paste, and a
single wrong byte makes every parse fail.
"""
from __future__ import annotations
import json
from typing import Any
def _tokens() -> dict[str, str]:
try:
import encoding_dsv4 as enc
except ImportError as exc: # pragma: no cover - environment dependent
raise RuntimeError(
"The encoding module is required. Add the repository's encoding/ folder "
"to PYTHONPATH, e.g. PYTHONPATH=/workspace/project/encoding"
) from exc
return {
"dsml": enc.dsml_token,
"eos": enc.eos_token,
"thinking_start": enc.thinking_start_token,
"thinking_end": enc.thinking_end_token,
"tool_calls_block": enc.tool_calls_block_name,
}
def _parameter(name: str, value: Any) -> str:
dsml = _tokens()["dsml"]
is_str = isinstance(value, str)
rendered = value if is_str else json.dumps(value, ensure_ascii=False)
flag = "true" if is_str else "false"
return f'<{dsml}parameter name="{name}" string="{flag}">{rendered}</{dsml}parameter>'
def build_tool_call(name: str, arguments: dict[str, Any], reasoning: str = "") -> str:
"""Render one assistant turn that calls a single tool.
Reproduces the exact byte sequence the parser expects: a leading blank line,
one parameter per line, the closing invoke tag, and the EOS token.
In thinking mode the completion opens with the reasoning content and closes
it with the end tag. The opening `` thinking`` token belongs to the *prompt*,
so it must not appear here; the parser rejects it inside content.
"""
t = _tokens()
lines = [f'<{t["dsml"]}invoke name="{name}">']
for key, value in arguments.items():
lines.append(_parameter(key, value))
body = "\n".join(lines) + f"\n</{t['dsml']}invoke>"
call = (
f"\n\n<{t['dsml']}{t['tool_calls_block']}>\n{body}\n"
f"</{t['dsml']}{t['tool_calls_block']}>"
f"{t['eos']}"
)
if reasoning:
return f"{reasoning}{t['thinking_end']}{call}"
return call
def build_answer(content: str, reasoning: str = "") -> str:
"""Render one assistant turn that answers without calling a tool."""
t = _tokens()
if reasoning:
return f"{reasoning}{t['thinking_end']}{content}{t['eos']}"
return f"{content}{t['eos']}"
def build_tool_result(content: str) -> str:
"""Render a tool result body, matching the encoder's tool_output_template."""
return f"<tool_result>{content}</tool_result>"
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