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AI45Research
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Model Details
Model Provider
AI45Research
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
This matched Qwen3.5 SFT baseline is provided alongside the MARCH-based Safin-1 models described in these papers. It retains the native Qwen3.5 architecture.
Use the reviewed repository ID and a fixed commit revision. Install the recorded Python 3.11 / PyTorch 2.11 / Transformers 5.5.4 environment first; the code repository contains the complete installation recipes.
python
import osimport torchfrom transformers import AutoTokenizer, AutoModelForImageTextToText model_id = "AI45Research/Qwen3.5-4B-Intern50B-NemotronSFT30B"revision = os.environ["MODEL_REVISION"]tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)model = AutoModelForImageTextToText.from_pretrained( model_id, revision=revision, dtype=torch.bfloat16, device_map="auto")inputs = tokenizer.apply_chat_template( [{"role": "user", "content": "Explain memory anchors."}], add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",).to(model.device)outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))The pinned code and model configuration define the supported workload. HF Hub hosting does not itself enable the MARCH vLLM backend; accelerated vLLM inference uses the fork and launchers in the code repository. See AI45Lab/Safin-1 for software versions, training commands and numerical limitations. Use a fixed Hub commit revision for reproducibility. CPT provenance is undocumented and a formal quality score table is not supplied.
The weights use Apache-2.0, as selected by the maintainer. See LICENSE and NOTICE in this model repository. Training data keeps its own terms; this does not establish permissions for the unresolved CPT corpus.
Family-level ancestry: Qwen/Qwen3.5-4B → Intern-S2-derived CPT
step 5961 → SFT step 3600. The upstream training commit and
CPT provenance/permission record are unavailable. The listed public dataset
metadata covers the identifiable SFT source, not the entire training corpus.
See TRAINING_DATA.md for the ten-file recipe and accounting limits.
See EVALUATION.md for recovered historical records and their limits. They are not a verified score table for these release payloads. The code validation matrix records separate technical acceptance and numerical limits. SHA256SUMS covers all files in this model root except the checksum file itself, including every complete weight shard.
import osimport torchfrom transformers import AutoTokenizer, AutoModelForImageTextToText model_id = "AI45Research/Qwen3.5-4B-Intern50B-NemotronSFT30B"revision = os.environ["MODEL_REVISION"]tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)model = AutoModelForImageTextToText.from_pretrained( model_id, revision=revision, dtype=torch.bfloat16, device_map="auto")inputs = tokenizer.apply_chat_template( [{"role": "user", "content": "Explain memory anchors."}], add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",).to(model.device)outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))