初始化项目，由ModelHub XC社区提供模型

Model: Sharjeelbaig/ShaziLLM-1B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+- zh
+base_model: openbmb/MiniCPM5-1B
+base_model_relation: finetune
+pipeline_tag: text-generation
+tags:
+- shazillm
+- llama
+- text-generation
+- thinking
+- coding
+- instruction-following
+- conversational
+---
+
+# ShaziLLM-1B
+
+ShaziLLM-1B is a compact 1.08B-parameter instruction and reasoning language model designed for coding, structured instruction following, tool use, and local deployment. This repository contains the normal BF16 Transformers checkpoint.
+
+For lower-memory CPU and browser inference, use [ShaziLLM-1B-ONNX](https://huggingface.co/Sharjeelbaig/ShaziLLM-1B-ONNX).
+
+## Quick start
+
+```bash
+pip install -U transformers torch accelerate
+```
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Sharjeelbaig/ShaziLLM-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+    )
+
+new_tokens = output[0, inputs.input_ids.shape[1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+## Model details
+
+| Item | Value |
+|---|---:|
+| Parameters | 1.08B |
+| Layers | 24 |
+| Hidden size | 1536 |
+| Attention heads / KV heads | 16 / 2 |
+| Vocabulary | 130,560 |
+| Maximum configured context | 131,072 tokens |
+| Weight format | BF16 Safetensors |
+| Architecture | Llama-compatible causal language model |
+
+The configured context limit is not a practical memory recommendation. Start with a much shorter context on low-memory hardware and increase it only after measuring KV-cache usage.
+
+## ShaziLLM alignment
+
+The release received a focused identity-alignment and capability-retention pass before export. A rank-8 LoRA adapter targeting the query and value projections was trained for four epochs at a `1e-4` learning rate on 48 short examples, with 1,032,192 trainable parameters. The adapter was merged into the BF16 checkpoint. Validation probes covered model identity, arithmetic, coding, and factual recall.
+
+## Capabilities
+
+- Code generation and debugging
+- Instruction following and structured responses
+- Reasoning-style responses
+- XML-formatted tool calls through the included chat template
+- English and Chinese text generation
+
+## Low-hardware deployment
+
+The BF16 checkpoint requires roughly 2.1 GB for weights alone, plus runtime and KV-cache memory. The separate ONNX INT8 release reduces weight storage and CPU inference memory substantially. Limit context and generated-token counts on constrained devices.
+
+## Limitations
+
+ShaziLLM-1B can hallucinate, produce incorrect code, and expose reasoning-like text. Do not treat its output as professional, security, legal, medical, or financial advice. Validate generated code before execution and isolate tool access behind deterministic authorization checks.
+
+## License and attribution
+
+Released under Apache License 2.0; see `LICENSE`. ShaziLLM-1B is a fine-tuned derivative of [OpenBMB/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B). The ShaziLLM name identifies this release and does not imply authorship of the upstream base architecture.