diff --git a/Shell/eval_chair.sh b/Shell/eval_chair.sh
index 70d53f0..586da9e 100755
--- a/Shell/eval_chair.sh
+++ b/Shell/eval_chair.sh
@@ -1,5 +1,46 @@
+#!/bin/bash
+# CHAIR Evaluation Script for FarSight
+# This script runs caption generation using FarSight decoding strategy
+
+# Configuration - Set these environment variables before running:
+# MODEL_PATH: Path to the LLaVA model (default: liuhaotian/llava-v1.5-7b)
+# IMAGE_FOLDER: Path to COCO val2014 images
+# QUESTION_FILE: Path to CHAIR questions file (JSONL format)
+# ANSWERS_FILE: Path to save generated captions (default: ./Answers/chair_captions.jsonl)
+
+MODEL_PATH="${MODEL_PATH:-liuhaotian/llava-v1.5-7b}"
+IMAGE_FOLDER="${IMAGE_FOLDER:-}"
+QUESTION_FILE="${QUESTION_FILE:-}"
+ANSWERS_FILE="${ANSWERS_FILE:-./Answers/chair_captions.jsonl}"
+
+# Validate required paths
+if [ -z "$IMAGE_FOLDER" ]; then
+    echo "Error: IMAGE_FOLDER environment variable is not set"
+    echo "Usage: IMAGE_FOLDER=/path/to/coco/val2014 QUESTION_FILE=/path/to/questions.jsonl bash eval_chair.sh"
+    exit 1
+fi
+
+if [ -z "$QUESTION_FILE" ]; then
+    echo "Error: QUESTION_FILE environment variable is not set"
+    echo "Usage: IMAGE_FOLDER=/path/to/coco/val2014 QUESTION_FILE=/path/to/questions.jsonl bash eval_chair.sh"
+    exit 1
+fi
+
+# Create output directory if needed
+mkdir -p "$(dirname "$ANSWERS_FILE")"
+
+# Step 1: Generate captions using FarSight
 python eval_chair.py \
---coco_path /root/autodl-tmp/annotations \
---cache ./chair.pkl \
---cap_file ./1.jsonl \
---save_path ./Answers/eval-chair.json
\ No newline at end of file
+    --model-path "$MODEL_PATH" \
+    --image-folder "$IMAGE_FOLDER" \
+    --question-file "$QUESTION_FILE" \
+    --answers-file "$ANSWERS_FILE" \
+    --farsight
+
+# Note: After generating captions, you can evaluate CHAIR metrics using:
+# - External CHAIR evaluation tool (e.g., https://github.com/Maxlinn/CHAIR-metric-standalone)
+# - Example command:
+#   python chair.py --cap_file ./Answers/chair_captions.jsonl \
+#       --coco_path /path/to/coco/annotations \
+#       --cache ./chair.pkl \
+#       --save_path ./Answers/eval-chair.json
\ No newline at end of file
diff --git a/Shell/farsight_patch.py b/Shell/farsight_patch.py
index 17244f1..4061056 100644
--- a/Shell/farsight_patch.py
+++ b/Shell/farsight_patch.py
@@ -4,11 +4,43 @@
 import torch.nn.functional as F
 import types
 
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    """Apply rotary position embedding to query and key tensors."""
+    # Handle different shapes of cos and sin from rotary embeddings
+    # Expected final shapes: cos, sin -> [bs, 1, seq_len, dim]
+    while cos.dim() > 2 and cos.shape[0] == 1:
+        cos = cos.squeeze(0)
+    while sin.dim() > 2 and sin.shape[0] == 1:
+        sin = sin.squeeze(0)
+    
+    # cos, sin should now be [seq_len, dim] or [1, seq_len, dim]
+    if cos.dim() == 2:
+        # [seq_len, dim] -> index by position_ids and add head dimension
+        cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+        sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    elif cos.dim() == 3:
+        # [1, seq_len, dim] -> index by position_ids
+        cos = cos.squeeze(0)[position_ids].unsqueeze(1)
+        sin = sin.squeeze(0)[position_ids].unsqueeze(1)
+    
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
 def farsight_attention_forward_v2(
     self,
     hidden_states,
     attention_mask=None,
-    position_ids=None,           # 接住但不使用
+    position_ids=None,
     past_key_value=None,         # 兼容 KV 缓存接口
     output_attentions=False,     # 是否需要返回注意力矩阵
     use_cache=False,             # 兼容 generate 传参；本实现不支持
@@ -26,12 +58,29 @@ def farsight_attention_forward_v2(
     dtype = hidden_states.dtype
     device = hidden_states.device
 
-    # Q K V
+    # Q K V projections
+    # Handle both standard attention and grouped-query attention (GQA)
+    num_key_value_heads = getattr(self, 'num_key_value_heads', self.num_heads)
+    
     q = self.q_proj(hidden_states).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
-    k = self.k_proj(hidden_states).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
-    v = self.v_proj(hidden_states).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
-
-    # 原始分数
+    k = self.k_proj(hidden_states).view(B, L, num_key_value_heads, self.head_dim).transpose(1, 2)
+    v = self.v_proj(hidden_states).view(B, L, num_key_value_heads, self.head_dim).transpose(1, 2)
+
+    # Apply rotary position embeddings (critical for LLaMA!)
+    if hasattr(self, 'rotary_emb'):
+        kv_seq_len = k.shape[-2]
+        cos, sin = self.rotary_emb(k, seq_len=kv_seq_len)
+        if position_ids is None:
+            position_ids = torch.arange(L, device=device).unsqueeze(0)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+    
+    # Handle grouped query attention (GQA) - repeat KV heads if needed
+    if num_key_value_heads < self.num_heads:
+        num_key_value_groups = self.num_heads // num_key_value_heads
+        k = k.repeat_interleave(num_key_value_groups, dim=1)
+        v = v.repeat_interleave(num_key_value_groups, dim=1)
+
+    # Compute attention scores
     attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)  # [B,H,L,L]
 
     # 因果下三角 C（两次用）
@@ -109,7 +158,37 @@ def _derive_valid_from_attention_mask(m, B, L, device, out_dtype):
     # ===== padding-safe 结束 =====
 
     # 组装 W 与 softmax
+    # FarSight formula: W = (QK^T/√d) ⊙ C + P
+    # where C is causal mask (lower triangular 1s), P is FarSight upper triangular penalties
+    
+    # Create a large negative value for masking (using -1e9 instead of -inf to avoid NaN issues)
+    NEG_INF = -1e9
+    
+    # For the lower triangular (causal): keep original attention scores
+    # For the upper triangular (future): apply FarSight penalties (allows limited look-ahead)
+    # The original code: attn_scores * C zeros out upper triangular completely,
+    # then adds P_total which has penalties only in upper triangular.
+    # This effectively gives: lower_tri = original_scores, upper_tri = penalties
+    
     W = attn_scores * C + P_total
+    
+    # Apply attention mask for padding tokens if provided
+    # The attention_mask from HuggingFace models is typically in additive format (0 for attend, -inf for mask)
+    if attention_mask is not None:
+        # Handle different attention mask formats from HuggingFace
+        if attention_mask.dim() == 4 and attention_mask.shape[-2] == L and attention_mask.shape[-1] == L:
+            # [B, 1, L, L] format - additive mask with 0 or -inf values
+            W = W + attention_mask.to(dtype)
+        elif attention_mask.dim() == 4 and attention_mask.shape[-2] == 1:
+            # [B, 1, 1, L] format - broadcast over query dimension
+            W = W + attention_mask.to(dtype)
+        elif attention_mask.dim() == 2:
+            # [B, L] format - binary mask (1 for attend, 0 for mask)
+            # Convert to additive mask format [B, 1, 1, L]
+            expanded_mask = attention_mask.unsqueeze(1).unsqueeze(2).to(dtype)
+            additive_mask = (1.0 - expanded_mask) * NEG_INF
+            W = W + additive_mask
+    
     attn_probs = torch.softmax(W, dim=-1) * C
 
     # 输出