初始化项目，由ModelHub XC社区提供模型

Model: danivpv/Llama-ML-Expert-Instruct-1b
Source: Original Platform

README.md

@@ -0,0 +1,81 @@
+---
+license: llama3.2
+base_model: unsloth/Llama-3.2-1B-bnb-4bit
+tags:
+- unsloth
+- sft
+- trl
+- lora
+- arxiv
+- text-generation
+- domain-expert
+- machine-learning
+datasets:
+- danivpv/ml-arxiv-instruct
+- mlabonne/FineTome-Alpaca-100k
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Llama-ML-Expert-Instruct-1b
+
+An instruction fine-tuned Small Language Model (SLM) specialized in Machine Learning
+domain expertise. Fine-tuned from `unsloth/Llama-3.2-1B-bnb-4bit` using LoRA adapters —
+efficient enough to run on modest hardware while retaining strong ML-domain reasoning.
+
+Part of the [LLM-ArXiv-Domain-Expert](https://github.com/danivpv/LLM-ArXiv-Domain-Expert)
+pipeline, which builds domain-expert LLMs from ArXiv papers end to end: paper parsing →
+instruction/preference dataset generation → SFT → (optional) DPO.
+
+## Model Details
+
+| | |
+| :--- | :--- |
+| **Base model** | [`unsloth/Llama-3.2-1B-bnb-4bit`](https://huggingface.co/unsloth/Llama-3.2-1B-bnb-4bit) (Llama 3.2, 1B) |
+| **Fine-tuning method** | LoRA — rank 32, alpha 32, dropout 0.0 |
+| **LoRA target modules** | `q_proj, k_proj, v_proj, up_proj, down_proj, o_proj, gate_proj` |
+| **Optimizer** | `adamw_8bit` |
+| **Prompt format** | Alpaca (`### Instruction: ... ### Response: ...`) |
+
+## Training Data
+
+- [`danivpv/ml-arxiv-instruct`](https://huggingface.co/datasets/danivpv/ml-arxiv-instruct) — instruction-answer pairs synthesized from ArXiv ML papers
+- 10,000-sample subset of [`mlabonne/FineTome-Alpaca-100k`](https://huggingface.co/datasets/mlabonne/FineTome-Alpaca-100k) — preserves general instruction-following alongside domain specialization
+
+## How to Use
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="danivpv/Llama-ML-Expert-Instruct-1b",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+prompt = """### Instruction:
+{your ML question here}
+
+### Response:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- 1B-parameter base model: strong for domain-specialized short-form Q&A, not a
+  general-purpose reasoning or long-context model.
+- Trained on synthetically generated instruction data (see
+  [`ml-arxiv-instruct`](https://huggingface.co/datasets/danivpv/ml-arxiv-instruct) for
+  the generation pipeline) — inherits whatever biases or gaps exist in the source LLM
+  used to generate that data.
+
+## Related
+
+- Dataset (SFT): [`danivpv/ml-arxiv-instruct`](https://huggingface.co/datasets/danivpv/ml-arxiv-instruct)
+- Dataset (DPO): [`danivpv/ml-arxiv-dpo`](https://huggingface.co/datasets/danivpv/ml-arxiv-dpo)
+- Pipeline code: [github.com/danivpv/LLM-ArXiv-Domain-Expert](https://github.com/danivpv/LLM-ArXiv-Domain-Expert)