Text Generation
Transformers
Safetensors
English
qwen3_5_text
qwen3.5
tinyllama
riscv
int4
in-weights
compiled-weights
exact-arithmetic
qwenti
conversational
Instructions to use OpceanAI/qwenti-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpceanAI/qwenti-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpceanAI/qwenti-inference") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpceanAI/qwenti-inference") model = AutoModelForCausalLM.from_pretrained("OpceanAI/qwenti-inference", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpceanAI/qwenti-inference with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpceanAI/qwenti-inference" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpceanAI/qwenti-inference", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpceanAI/qwenti-inference
- SGLang
How to use OpceanAI/qwenti-inference 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 "OpceanAI/qwenti-inference" \ --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": "OpceanAI/qwenti-inference", "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 "OpceanAI/qwenti-inference" \ --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": "OpceanAI/qwenti-inference", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpceanAI/qwenti-inference with Docker Model Runner:
docker model run hf.co/OpceanAI/qwenti-inference
File size: 10,982 Bytes
5b8649d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | """Build a print-quality PDF from PAPER-tinyllama-rv32.md.
python make_paper_pdf.py [PAPER.md] [OUT.pdf]
Renders headings, paragraphs, bullets, tables, fenced code, figures and captions on A4 with
running heads, page numbers and a title block. No LaTeX, no browser.
"""
import os
import re
import sys
from reportlab.lib import colors
from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY, TA_LEFT
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
from reportlab.lib.units import mm
from reportlab.platypus import (BaseDocTemplate, Frame, Image, KeepTogether, PageBreak,
PageTemplate, Paragraph, Spacer, Table, TableStyle)
SRC = sys.argv[1] if len(sys.argv) > 1 else "PAPER-tinyllama-rv32.md"
OUT = sys.argv[2] if len(sys.argv) > 2 else "PAPER-tinyllama-rv32.pdf"
BASE = os.path.dirname(os.path.abspath(SRC))
INK = colors.HexColor("#111111")
MUTED = colors.HexColor("#555555")
RULE = colors.HexColor("#bbbbbb")
ACCENT = colors.HexColor("#1f3a5f")
CODEBG = colors.HexColor("#f4f5f7")
ss = getSampleStyleSheet()
TITLE = ParagraphStyle("t", parent=ss["Title"], fontName="Times-Bold", fontSize=21, leading=25,
textColor=INK, spaceAfter=6, alignment=TA_LEFT)
SUBTITLE = ParagraphStyle("st", parent=ss["Normal"], fontName="Times-Italic", fontSize=11.5,
leading=15, textColor=MUTED, spaceAfter=2)
H1 = ParagraphStyle("h1", parent=ss["Heading1"], fontName="Times-Bold", fontSize=14, leading=18,
textColor=ACCENT, spaceBefore=14, spaceAfter=6, keepWithNext=1)
H2 = ParagraphStyle("h2", parent=ss["Heading2"], fontName="Times-Bold", fontSize=11.5, leading=15,
textColor=INK, spaceBefore=10, spaceAfter=4, keepWithNext=1)
BODY = ParagraphStyle("b", parent=ss["BodyText"], fontName="Times-Roman", fontSize=10, leading=14.2,
alignment=TA_JUSTIFY, spaceAfter=5)
BULLET = ParagraphStyle("bl", parent=BODY, leftIndent=12, bulletIndent=2, spaceAfter=3)
QUOTE = ParagraphStyle("q", parent=BODY, fontName="Times-Italic", leftIndent=10, rightIndent=6,
textColor=colors.HexColor("#333333"), spaceBefore=2, spaceAfter=6)
CODE = ParagraphStyle("c", parent=ss["Code"], fontName="Courier", fontSize=7.4, leading=9.4,
textColor=INK, backColor=CODEBG, borderPadding=(4, 5, 4, 5),
leftIndent=6, rightIndent=6, spaceBefore=3, spaceAfter=7)
CAPTION = ParagraphStyle("cap", parent=ss["Normal"], fontName="Times-Italic", fontSize=8.4,
leading=11, textColor=MUTED, alignment=TA_CENTER, spaceBefore=3,
spaceAfter=9)
CELL = ParagraphStyle("cell", parent=ss["Normal"], fontName="Times-Roman", fontSize=8.2,
leading=10.4, textColor=INK)
CELLH = ParagraphStyle("cellh", parent=CELL, fontName="Times-Bold")
CELLR = ParagraphStyle("cellr", parent=CELL, alignment=2)
def inline(text):
text = text.replace("&", "&").replace("<", "<").replace(">", ">")
text = re.sub(r"`([^`]+)`", r'<font name="Courier" size="8.6">\1</font>', text)
text = re.sub(r"\*\*([^*]+)\*\*", r"<b>\1</b>", text)
text = re.sub(r"(?<!\*)\*([^*]+)\*(?!\*)", r"<i>\1</i>", text)
text = re.sub(r"\[([^\]]+)\]\(([^)]+)\)", r'<font color="#1f3a5f">\1</font>', text)
return text
def is_number(cell):
c = cell.strip().replace(",", "").replace("%", "").replace("**", "")
try:
float(c)
return True
except ValueError:
return False
def table_from(rows):
has_head = len(rows) > 1 and not all(set(c) <= set("-: ") for c in rows[1])
head = rows[0] if has_head else None
body = rows[1:] if has_head else rows
data = []
if head:
data.append([Paragraph(inline(c), CELLH) for c in head])
for r in body:
data.append([Paragraph(inline(c), CELLR if is_number(c) and i else CELL)
for i, c in enumerate(r)])
ncol = max(len(r) for r in data)
for r in data:
while len(r) < ncol:
r.append(Paragraph("", CELL))
width = 170 * mm
first = 58 * mm if ncol > 2 else width / ncol
cols = [first] + [(width - first) / (ncol - 1)] * (ncol - 1) if ncol > 1 else [width]
t = Table(data, colWidths=cols, hAlign="LEFT", repeatRows=1 if head else 0)
style = [("VALIGN", (0, 0), (-1, -1), "TOP"),
("TOPPADDING", (0, 0), (-1, -1), 3),
("BOTTOMPADDING", (0, 0), (-1, -1), 3),
("LEFTPADDING", (0, 0), (-1, -1), 5),
("RIGHTPADDING", (0, 0), (-1, -1), 5),
("LINEBELOW", (0, 0), (-1, -1), 0.25, RULE)]
if head:
style += [("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#eceff3")),
("LINEABOVE", (0, 0), (-1, 0), 0.9, ACCENT),
("LINEBELOW", (0, 0), (-1, 0), 0.6, ACCENT)]
t.setStyle(TableStyle(style))
return t
def figure(path):
full = os.path.join(BASE, path)
if not os.path.exists(full):
return None
from PIL import Image as PILImage
w, h = PILImage.open(full).size
width = min(150 * mm, w * 0.75)
return Image(full, width=width, height=width * h / w)
def parse(md):
flow = []
lines = md.splitlines()
i = 0
in_code = False
code_buf = []
title_done = False
para = []
bullets = []
table = []
def flush_para():
nonlocal para
if para:
flow.append(Paragraph(inline(" ".join(para)), BODY))
para = []
def flush_bullets():
nonlocal bullets
for b in bullets:
flow.append(Paragraph(inline(b), BULLET, bulletText="\u2022"))
if bullets:
flow.append(Spacer(1, 2))
bullets = []
def flush_table():
nonlocal table
if table:
flow.append(table_from(table))
flow.append(Spacer(1, 7))
table = []
while i < len(lines):
ln = lines[i]
stripped = ln.strip()
if stripped.startswith("```"):
flush_para(); flush_bullets(); flush_table()
if in_code:
flow.append(Paragraph("<br/>".join(c.replace(" ", " ") for c in code_buf)
.replace(" ", " "),
CODE))
code_buf = []
in_code = False
else:
in_code = True
i += 1
continue
if in_code:
code_buf.append(ln if ln else " ")
i += 1
continue
if not stripped:
flush_para(); flush_bullets(); flush_table()
i += 1
continue
if stripped.startswith("|"):
flush_para(); flush_bullets()
cells = [c.strip() for c in stripped.strip("|").split("|")]
if not all(set(c) <= set("-: ") for c in cells):
table.append(cells)
i += 1
continue
m = re.match(r"^!\[([^\]]*)\]\(([^)]+)\)", stripped)
if m:
flush_para(); flush_bullets(); flush_table()
cap = m.group(1)
j = i + 1
while j < len(lines) and not lines[j].strip():
j += 1
if j < len(lines):
nxt = lines[j].strip()
mm = re.match(r"^\*?(Figure\s+\d+\.[^*]*)\*?$", nxt.lstrip("*_").rstrip("*_"))
if mm:
cap = mm.group(1)
i = j
img = figure(m.group(2))
if img:
flow.append(KeepTogether([img, Paragraph(inline(cap), CAPTION)]))
i += 1
continue
m = re.match(r"^#\s+(.*)$", stripped)
if m and not title_done:
flush_para(); flush_bullets(); flush_table()
flow.append(Paragraph(inline(m.group(1)), TITLE))
title_done = True
i += 1
continue
m = re.match(r"^##\s+(.*)$", stripped)
if m:
flush_para(); flush_bullets(); flush_table()
flow.append(Paragraph(inline(m.group(1)), H1))
i += 1
continue
m = re.match(r"^###\s+(.*)$", stripped)
if m:
flush_para(); flush_bullets(); flush_table()
flow.append(Paragraph(inline(m.group(1)), H2))
i += 1
continue
if stripped.startswith(">"):
flush_para(); flush_bullets(); flush_table()
flow.append(Paragraph(inline(stripped.lstrip("> ").strip()), QUOTE))
i += 1
continue
m = re.match(r"^[-*]\s+(.*)$", stripped)
if m:
flush_para(); flush_table()
bullets.append(m.group(1))
i += 1
continue
m = re.match(r"^\d+\.\s+(.*)$", stripped)
if m:
flush_para(); flush_table()
bullets.append(m.group(1))
i += 1
continue
if bullets and (ln.startswith(" ") or ln.startswith("\t")):
bullets[-1] = bullets[-1] + " " + stripped
i += 1
continue
if stripped in ("---", "***"):
flush_para(); flush_bullets(); flush_table()
flow.append(Spacer(1, 6))
i += 1
continue
if title_done and not para and not flow[-1:] == [None] and \
getattr(flow[-1], "style", None) is TITLE:
flow.append(Paragraph(inline(stripped), SUBTITLE))
i += 1
continue
para.append(stripped)
i += 1
flush_para(); flush_bullets(); flush_table()
return flow
def decorate(canvas, doc):
canvas.saveState()
canvas.setFont("Times-Roman", 8)
canvas.setFillColor(MUTED)
if doc.page > 1:
canvas.drawString(20 * mm, A4[1] - 13 * mm,
"TinyLLama-v0 as an int4 workload on a token-level RISC-V machine")
canvas.setStrokeColor(RULE)
canvas.setLineWidth(0.3)
canvas.line(20 * mm, A4[1] - 15 * mm, A4[0] - 20 * mm, A4[1] - 15 * mm)
canvas.drawRightString(A4[0] - 20 * mm, 12 * mm, str(doc.page))
canvas.restoreState()
def main():
md = open(SRC, encoding="utf-8").read()
flow = parse(md)
doc = BaseDocTemplate(OUT, pagesize=A4, leftMargin=20 * mm, rightMargin=20 * mm,
topMargin=20 * mm, bottomMargin=18 * mm,
title="TinyLLama-v0 as an int4 workload on a token-level RISC-V machine",
author="OpceanAI", subject="int4 inference on a token-level RV32 machine")
frame = Frame(doc.leftMargin, doc.bottomMargin, doc.width, doc.height, id="f")
doc.addPageTemplates([PageTemplate(id="p", frames=[frame], onPage=decorate)])
doc.build(flow)
print("escrito", OUT, os.path.getsize(OUT) // 1024, "KB")
if __name__ == "__main__":
main()
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