初始化项目，由ModelHub XC社区提供模型

Model: transformers-community/contrastive-search
Source: Original Platform

README.md

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+---
+
+library_name: transformers
+tags:
+  - custom_generate
+---
+
+## Description
+Implementation of [Contrastive Search](https://huggingface.co/blog/introducing-csearch), a decoding strategy that jointly optimizes model confidence and a degeneration penalty to produce fluent, coherent, and low-repetition text. At each step, the model considers the top-k candidate tokens and selects the one maximizing:
+
+score(v) = (1 - alpha) * p(v | context) - alpha * max_cosine_similarity(h_v, H_context)
+
+where `alpha` is the trade-off between confidence and the cosine-similarity-based penalty.
+
+This strategy typically:
+
+- Reduces repetition compared to greedy/beam search
+- Preserves semantic coherence better than pure sampling
+
+---
+
+## Base model
+
+- `Qwen/Qwen2.5-0.5B-Instruct` (example)
+
+---
+
+## Model compatibility
+
+- Decoder and encoder-decoder transformer models for causal LM
+
+---
+
+## Additional Arguments
+
+- `top_k` (int): Number of candidate tokens to consider each step (e.g., 4)
+- `penalty_alpha` (float): Weight of the degeneration penalty (e.g., 0.6)
+
+Tips:
+- Larger `top_k` explores more candidates but increases compute
+- `penalty_alpha` in [0.3, 0.8] often works well; `0.0` reduces to greedy
+
+---
+
+## Output Type changes
+
+(none) — returns the same structure as standard `transformers` generation
+
+---
+
+## Example usage
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
+
+device = infer_device()
+
+model_id = "Qwen/Qwen2.5-0.5B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto").to(device)
+
+inputs = tokenizer(["DeepMind Company is"], return_tensors="pt").to(device)
+
+# Contrastive search
+gen_out = model.generate(
+    **inputs,
+    custom_generate="contrastive_search",
+    penalty_alpha=0.6,
+    top_k=4,
+    max_new_tokens=128,
+    trust_remote_code=True,
+)
+
+print(tokenizer.batch_decode(gen_out, skip_special_tokens=True))
+```