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
Download scripts/circheck.py from OpceanAI/qwenti-inference: direct link, hf CLI and curl.
- Browser
- Download file 2.18 kB
-
https://huggingface.co/OpceanAI/qwenti-inference/resolve/main/scripts/circheck.py
- Command line
-
hf download hf://OpceanAI/qwenti-inference/scripts/circheck.py
-
curl -L -o circheck.py https://huggingface.co/OpceanAI/qwenti-inference/resolve/main/scripts/circheck.py
2.18 kB
| import collections | |
| import copy | |
| import os | |
| import subprocess | |
| import sys | |
| import time | |
| os.environ.setdefault("CHIPFORMER_WROM_HEADS", "320") | |
| sys.path.insert(0, "/tmp/opencode/qp/experimental/riscv") | |
| from chipformer.q35 import ir | |
| from chipformer.rv import core, elf, ref | |
| from chipformer.rv import spec as S | |
| path, where, ntok = sys.argv[1], sys.argv[2], int(sys.argv[3]) | |
| syms = {} | |
| for line in subprocess.run(["llvm-nm", path], capture_output=True, text=True).stdout.splitlines(): | |
| parts = line.split() | |
| if len(parts) == 3: | |
| syms[parts[2]] = int(parts[0], 16) | |
| m = ref.Machine.from_program(elf.program(path)) | |
| dump = lambda i: m.ram[0xE000 // 4 + i] | |
| def op_at(mm): | |
| if mm.n == S.N_MAX: | |
| return None | |
| return ref.decode(mm.rom[(mm.pc - S.ROM_BASE) >> 2]).op | |
| def advance(pred): | |
| while not m.halted and not pred(): | |
| m.record() | |
| assert not m.halted, "el programa termino antes del punto de control" | |
| if where == "start": | |
| pass | |
| elif where == "bank": | |
| advance(lambda: m.bank >= 1) | |
| elif where == "down": | |
| advance(lambda: m.pc == syms["vk_8_init_raw"]) | |
| elif where == "pos1_attn": | |
| advance(lambda: dump(100) != 0) | |
| advance(lambda: op_at(m) == "SMAQA") | |
| elif where == "head_bank": | |
| advance(lambda: dump(30) != 0) | |
| b0 = m.bank | |
| advance(lambda: m.bank != b0) | |
| print(f"punto de control {where}: instrucciones ejecutadas {m.steps}, pc {m.pc:#x}, banco {m.bank}, wb {m.wb}", flush=True) | |
| ref_m = copy.deepcopy(m) | |
| mix = collections.Counter() | |
| toks = [S.tok("GO")] | |
| while len(toks) < ntok and not ref_m.halted: | |
| mix[op_at(ref_m)] += 1 | |
| toks += ref_m.record() | |
| print("mezcla de instrucciones del tramo:", dict(mix.most_common(14)), flush=True) | |
| t0 = time.time() | |
| c = core.build() | |
| core.snapshot(c, m) | |
| mat = ir.evaluate(c, toks, check=True) | |
| pred = core.predictions(c, mat) | |
| bad = [(i, S.name(toks[i + 1]), pred[i]) for i in range(len(toks) - 1) if pred[i] != toks[i + 1]] | |
| fams = collections.Counter(S.family(t)[0] for t in toks) | |
| print(f"tokens {len(toks)}, tokens WB {fams['WB']}, WBB {fams['WBB']}, desacuerdos {len(bad)}, {time.time() - t0:.0f}s", flush=True) | |
| print("CIRCUITO == REFERENCIA" if not bad else f"FALLA: {bad[:5]}", flush=True) | |