初始化项目，由ModelHub XC社区提供模型

Model: nehmeailabs-org/nehme-flashcheck-1b Source: Original Platform
2026-05-31 13:58:19 +08:00
commit 1a9f59fef6
11 changed files with 239 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,130 @@
+---
+license: gemma
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- fact-checking
+- hallucination-detection
+- rag
+- compliance
+- guardrails
+- nli
+- gemma
+base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
+---
+
+# FlashCheck-1B: The Enterprise Logic Engine
+
+## Model Description
+
+**FlashCheck-1B** is a Gemma 3 (1B) fine-tune specialized for **Contextual Policy Adherence** and **Hallucination Detection**.
+
+It is designed to act as a fast verifier in RAG pipelines: given a **Document** and a **Claim**, it answers **"Yes"** if the claim is fully supported by the document, otherwise **"No"**.
+
+- **Developer:** Nehme AI Labs
+- **Training Base:** `unsloth/gemma-3-1b-it-unsloth-bnb-4bit` (Gemma family)
+- **License/Terms:** Gemma (see Gemma terms associated with the base model)
+
+## What’s in this repo
+
+- **Transformers (standalone):** `config.json` + `model.safetensors` + tokenizer files
+- **GGUF (local inference):** `nehme-flashcheck-1b.Q8_0.gguf` (or in `gguf/` if you placed it there)
+
+## Intended behavior
+
+- Input: **Document** (premise) + **Claim** (hypothesis)
+- Output: **"Yes"** or **"No"** (short, deterministic; use greedy decoding)
+
+## Usage
+
+### 1) Python (Transformers)
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "nehmeailabs-org/nehme-flashcheck-1b"
+
+SYSTEM_MESSAGE = (
+    "You are a fact checking model developed by NehmeAILabs. Determine whether the provided claim is consistent with "
+    "the corresponding document. Consistency in this context implies that all information presented in the claim is "
+    "substantiated by the document. If not, it should be considered inconsistent. Please assess the claim's consistency "
+    "with the document by responding with either \"Yes\" or \"No\"."
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype="auto",
+)
+model.eval()
+
+document = "The user must not share API keys."
+claim = "The user message 'Here is the staging key sk-123' violates the policy."
+
+user_prompt = f"Document: {document}\n\nClaim: {claim}"
+
+messages = [
+    {"role": "system", "content": SYSTEM_MESSAGE},
+    {"role": "user", "content": user_prompt},
+]
+
+try:
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    )
+except Exception:
+    plain = f"{SYSTEM_MESSAGE}\n\n{user_prompt}"
+    input_ids = tokenizer(plain, return_tensors="pt").input_ids
+
+input_ids = input_ids.to(model.device)
+
+with torch.no_grad():
+    out = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=8,
+        do_sample=False,
+        temperature=0.0,
+        top_p=1.0,
+    )
+
+gen_ids = out[0, input_ids.shape[-1]:]
+verdict = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
+print(verdict)  # Expected: "Yes" or "No"
+```
+
+### 2) Local (GGUF / llama.cpp)
+
+If the GGUF file is at repo root:
+
+```bash
+./main -m nehme-flashcheck-1b.Q8_0.gguf -p "Document: ...\n\nClaim: ..."
+```
+
+If you placed it in a `gguf/` folder:
+
+```bash
+./main -m gguf/nehme-flashcheck-1b.Q8_0.gguf -p "Document: ...\n\nClaim: ..."
+```
+
+## Notes
+
+- For best results, keep the prompt format stable (`Document:` then `Claim:`) and use deterministic decoding.
+- This model is optimized for verification/consistency checks, not general open-ended chat.
+
+## Citation
+
+```bibtex
+@misc{nehme2025flashcheck,
+  title={FlashCheck: Efficient Logic Distillation for RAG Compliance},
+  author={NehmeAILabs},
+  year={2025},
+  publisher={Nehme AI Labs},
+  howpublished={\url{https://nehmeailabs.com}}
+}
+```
+