Text Generation
Transformers
Safetensors
PyTorch
English
qwen3
qwen
qwen3-1.7b
qwen3-8b
quintus
quintus-1.7b
causal-lm
language-model
chat
assistant
compact-llm
small-language-model
knowledge-distillation
online-kd
full-vocabulary-kd
supervised-fine-tuning
sft
reasoning
code-generation
english
vllm
conversational
text-generation-inference
Instructions to use iamrahulreddy/Quintus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamrahulreddy/Quintus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="iamrahulreddy/Quintus") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("iamrahulreddy/Quintus") model = AutoModelForCausalLM.from_pretrained("iamrahulreddy/Quintus") 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use iamrahulreddy/Quintus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "iamrahulreddy/Quintus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "iamrahulreddy/Quintus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/iamrahulreddy/Quintus
- SGLang
How to use iamrahulreddy/Quintus 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 "iamrahulreddy/Quintus" \ --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": "iamrahulreddy/Quintus", "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 "iamrahulreddy/Quintus" \ --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": "iamrahulreddy/Quintus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use iamrahulreddy/Quintus with Docker Model Runner:
docker model run hf.co/iamrahulreddy/Quintus
| from __future__ import annotations | |
| import hashlib | |
| import json | |
| from pathlib import Path | |
| from typing import Any | |
| PROVENANCE_SCHEMA_VERSION = 4 | |
| _SHARD_SCHEMA = { | |
| "support": "teacher_topk_plus_other_bucket", | |
| "layout": "chunked_sample_lists", | |
| "logprobs_dtype": "float16", | |
| "ids_dtype": "int32", | |
| "other_logprob_dtype": "float16", | |
| } | |
| _SPECIAL_TOKEN_ID_FIELDS = ( | |
| "bos_token_id", | |
| "eos_token_id", | |
| "pad_token_id", | |
| "unk_token_id", | |
| "cls_token_id", | |
| "sep_token_id", | |
| "mask_token_id", | |
| "additional_special_tokens_ids", | |
| ) | |
| def normalize_config_revision(value: str | None) -> str | None: | |
| if value is None: | |
| return None | |
| stripped = value.strip() | |
| return stripped or None | |
| def canonical_revision(value: str | None) -> str: | |
| return normalize_config_revision(value) or "unversioned" | |
| def sha256_file(path: str | Path, chunk_size: int = 1 << 20) -> str: | |
| digest = hashlib.sha256() | |
| with open(path, "rb") as handle: | |
| while True: | |
| chunk = handle.read(chunk_size) | |
| if not chunk: | |
| break | |
| digest.update(chunk) | |
| return digest.hexdigest() | |
| def _special_token_ids(tokenizer) -> dict[str, Any]: | |
| snapshot: dict[str, Any] = {} | |
| for field in _SPECIAL_TOKEN_ID_FIELDS: | |
| value = getattr(tokenizer, field, None) | |
| if isinstance(value, tuple): | |
| value = list(value) | |
| snapshot[field] = value | |
| return snapshot | |
| def build_tokenizer_contract(tokenizer) -> dict[str, Any]: | |
| canonical = { | |
| "tokenizer_class": tokenizer.__class__.__name__, | |
| "full_vocab_size": len(tokenizer), | |
| "special_token_ids": _special_token_ids(tokenizer), | |
| "vocab": dict(sorted(tokenizer.get_vocab().items())), | |
| } | |
| encoded = json.dumps( | |
| canonical, | |
| sort_keys=True, | |
| separators=(",", ":"), | |
| ensure_ascii=True, | |
| ).encode("utf-8") | |
| return { | |
| "tokenizer_class": canonical["tokenizer_class"], | |
| "full_vocab_size": canonical["full_vocab_size"], | |
| "special_token_ids": canonical["special_token_ids"], | |
| "fingerprint": hashlib.sha256(encoded).hexdigest(), | |
| } | |
| def build_shard_schema() -> dict[str, str]: | |
| return dict(_SHARD_SCHEMA) | |
| def collect_model_vocab_sizes(model) -> dict[str, int]: | |
| sizes: dict[str, int] = {} | |
| config_size = getattr(getattr(model, "config", None), "vocab_size", None) | |
| if isinstance(config_size, int): | |
| sizes["config"] = config_size | |
| input_embeddings = model.get_input_embeddings() | |
| if input_embeddings is not None and getattr(input_embeddings, "weight", None) is not None: | |
| sizes["input_embeddings"] = int(input_embeddings.weight.shape[0]) | |
| output_embeddings = model.get_output_embeddings() | |
| if output_embeddings is not None and getattr(output_embeddings, "weight", None) is not None: | |
| sizes["output_embeddings"] = int(output_embeddings.weight.shape[0]) | |
| return sizes | |