初始化项目，由ModelHub XC社区提供模型

Model: SriRamanaAtmic/AtmicIntelv1
Source: Original Platform

handler.py

@@ -0,0 +1,111 @@
+"""
+handler.py — HuggingFace Inference Endpoint handler for SriRamanaAtmic/AtmicIntelv1
+Compatible with transformers==4.51.3 (matches model's transformers_version in config.json).
+
+Generation parameters (from Section A4 of technical review — do not change):
+  do_sample           = False  (greedy decoding — matches SFT + DPO training exactly)
+  max_new_tokens      = 350
+  repetition_penalty  = 1.0   (sole repetition control)
+  no_repeat_ngram_size = 0     (PERMANENTLY DISABLED — hard-coded, not overridable via API)
+  temperature / top_p          (REMOVED — inactive under greedy decoding)
+
+Token IDs (from added_tokens.json — verified):
+  <|endoftext|> = 32000  (pad_token_id)
+  <|assistant|> = 32001  (appears in INPUT prompt — must NEVER be eos_token_id)
+  <|end|>       = 32007  (turn terminator — correct eos for generation)
+
+Critical: generation_config.json in the repo contains eos_token_id=[32000, 32001, 32007].
+Token 32001 (<|assistant|>) is present in every input prompt, causing generation to stop
+at token 0. This handler explicitly overrides generation_config.json by setting
+self.model.generation_config before any generate() call.
+
+Input contract:
+  The caller (pipeline.py via prompt_builder.py) sends a fully-formatted Phi-3 prompt string.
+  This handler does NOT apply any chat template — prompt arrives ready to tokenize.
+  {"inputs": "<|system|>...<|end|>\n<|user|>...<|end|>\n<|assistant|>\n"}
+"""
+
+# ── DynamicCache compatibility shim (transformers >= 4.38) ──────────────────
+# Must be first — before any other transformers import.
+import transformers.cache_utils as _cu
+if not hasattr(_cu.DynamicCache, "get_max_length"):
+    _cu.DynamicCache.get_max_length = lambda self: None
+
+from transformers import DynamicCache
+if not hasattr(DynamicCache, "get_max_length"):
+    DynamicCache.get_max_length = lambda self: None
+# ────────────────────────────────────────────────────────────────────────────
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # ── Tokenizer ────────────────────────────────────────────────────
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            path,
+            trust_remote_code=True,
+        )
+
+        # ── Model ────────────────────────────────────────────────────────
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.bfloat16,      # matches config.json torch_dtype
+            device_map="auto",
+            trust_remote_code=True,
+            attn_implementation="eager",     # avoids flash-attn dependency
+        )
+        self.model.eval()
+
+        # ── Override generation_config.json ──────────────────────────────
+        # generation_config.json in the repo has eos_token_id=[32000, 32001, 32007].
+        # Token 32001 is <|assistant|>, which appears in every input prompt.
+        # This causes generate() to stop at token 0 — empty output.
+        # We override it here so model.generate() never reads the repo file.
+        self.model.generation_config = GenerationConfig(
+            do_sample=False,                 # greedy — matches SFT+DPO training
+            repetition_penalty=1.0,
+            no_repeat_ngram_size=0,          # permanently disabled
+            eos_token_id=32007,              # <|end|> only — turn terminator
+            pad_token_id=32000,              # <|endoftext|>
+            bos_token_id=1,
+        )
+
+    def __call__(self, data: dict) -> list:
+        # ── Input: fully-formatted prompt string from prompt_builder.py ──
+        inputs     = data.get("inputs", "")
+        parameters = data.get("parameters", {})
+
+        max_new_tokens     = int(parameters.get("max_new_tokens",     350))
+        repetition_penalty = float(parameters.get("repetition_penalty", 1.15))
+
+        # ── Tokenize — prompt already contains all special tokens ─────────
+        tokenized = self.tokenizer(
+            inputs,
+            return_tensors="pt",
+            truncation=True,
+            max_length=3500,           # leaves 596-token headroom within 4096
+            add_special_tokens=False,  # prompt_builder adds
+        ).to(self.model.device)
+
+        input_length = tokenized["input_ids"].shape[1]
+
+        # ── Generate ──────────────────────────────────────────────────────
+        # generation_config on the model is already overridden in __init__.
+        # kwargs here take final precedence for per-request overrides.
+        with torch.inference_mode():
+            output = self.model.generate(
+                **tokenized,
+                max_new_tokens=max_new_tokens,
+                repetition_penalty=repetition_penalty,
+                do_sample=False,
+                no_repeat_ngram_size=0,
+                eos_token_id=32007,        # <|end|> — confirmed turn terminator
+                pad_token_id=32000,
+            )
+
+        # ── Decode new tokens only ────────────────────────────────────────
+        new_tokens     = output[0][input_length:]
+        generated_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
+        return [{"generated_text": generated_text}]