commit ae3ded2cb7f01fef64ff8b35718cab1b08d7b9ad
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Fri May 29 23:45:21 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: dizza01/llama-3.1-8b-bib-grounded-sft-merged
    Source: Original Platform

diff --git a/.gitattributes b/.gitattributes
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
\ No newline at end of file
diff --git a/chat_template.jinja b/chat_template.jinja
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--- /dev/null
+++ b/chat_template.jinja
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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
diff --git a/config.json b/config.json
new file mode 100644
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--- /dev/null
+++ b/config.json
@@ -0,0 +1,94 @@
+{
+  "vocab_size": 128256,
+  "max_position_embeddings": 131072,
+  "hidden_size": 4096,
+  "intermediate_size": 14336,
+  "num_hidden_layers": 32,
+  "num_attention_heads": 32,
+  "num_key_value_heads": 8,
+  "hidden_act": "silu",
+  "initializer_range": 0.02,
+  "rms_norm_eps": 1e-05,
+  "pretraining_tp": 1,
+  "use_cache": true,
+  "rope_theta": 500000.0,
+  "rope_scaling": {
+    "factor": 8.0,
+    "low_freq_factor": 1.0,
+    "high_freq_factor": 4.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "mlp_bias": false,
+  "head_dim": 128,
+  "return_dict": true,
+  "output_hidden_states": false,
+  "output_attentions": false,
+  "torchscript": false,
+  "torch_dtype": "bfloat16",
+  "use_bfloat16": false,
+  "tf_legacy_loss": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": false,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "tokenizer_class": null,
+  "prefix": null,
+  "bos_token_id": 128000,
+  "pad_token_id": 128009,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "task_specific_params": null,
+  "problem_type": null,
+  "_attn_implementation_autoset": false,
+  "transformers_version": "4.51.3",
+  "model_type": "llama"
+}
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..3f93f46
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "pad_token_id": 128009,
+  "do_sample": false,
+  "repetition_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "renormalize_logits": true,
+  "max_new_tokens": 64
+}\n
\ No newline at end of file
diff --git a/handler.py b/handler.py
new file mode 100644
index 0000000..82854fe
--- /dev/null
+++ b/handler.py
@@ -0,0 +1,314 @@
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import AutoPeftModelForCausalLM
+
+DEFAULT_INSTRUCTION = (
+    "You are an expert research assistant for the Born in Bradford study. "
+    "Use only the provided context. "
+    "If the context contains the requested value, relationship, or finding, answer it directly; do not say it is absent. "
+    "Only answer exactly: The provided context does not contain the requested information after checking paper excerpts, labels, headings, rows, and adjacent context for an exact match. "
+    "For table-like or multi-finding context, choose the answer whose row label, column label, outcome, subgroup, timepoint, model, and measurement all match the question; ignore nearby rows where any anchor differs. "
+    "Follow the response rule after the question, because it identifies the exact extraction style needed. "
+    "Preserve all numbers, ranges, units, p-values, confidence intervals, dates, "
+    "distances, comparison groups, and categories exactly as written in the context. "
+    "Do not add page numbers, figure numbers, table positions, p-values, methods, "
+    "ranges, or qualifiers unless they appear in the provided context. "
+    "Answer only the requested value, relationship, or finding; do not include "
+    "adjacent table values, extra categories, variable metadata, or explanatory detail unless asked. "
+    "Do not add partial related information after abstaining."
+)
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        model_dir = path or "/repository"
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_dir,
+            trust_remote_code=True,
+        )
+        if self.tokenizer.pad_token_id is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.truncation_side = "left"
+
+        dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+        adapter_config_path = os.path.join(model_dir, "adapter_config.json")
+        if os.path.exists(adapter_config_path):
+            self.model = AutoPeftModelForCausalLM.from_pretrained(
+                model_dir,
+                trust_remote_code=True,
+                torch_dtype=dtype,
+                low_cpu_mem_usage=True,
+                device_map="auto" if torch.cuda.is_available() else None,
+            )
+        else:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_dir,
+                trust_remote_code=True,
+                torch_dtype=dtype,
+                low_cpu_mem_usage=True,
+                device_map="auto" if torch.cuda.is_available() else None,
+            )
+
+        self.model.eval()
+
+    def _coerce_messages(self, raw_messages):
+        messages = []
+        for message in raw_messages:
+            if not isinstance(message, dict):
+                continue
+            role = str(message.get("role", "")).strip()
+            content = message.get("content", "")
+            if role not in {"system", "user", "assistant"}:
+                continue
+            if content is None:
+                content = ""
+            messages.append({"role": role, "content": str(content)})
+        return messages
+
+    def _merge_system_into_first_user(self, messages):
+        system_text = "\n\n".join(
+            message["content"] for message in messages if message["role"] == "system"
+        ).strip()
+        non_system_messages = [message for message in messages if message["role"] != "system"]
+
+        if not system_text:
+            return non_system_messages
+        if not non_system_messages:
+            return [{"role": "user", "content": f"Instruction: {system_text}"}]
+
+        for index, message in enumerate(non_system_messages):
+            if message["role"] == "user":
+                non_system_messages[index] = {
+                    "role": "user",
+                    "content": f"Instruction: {system_text}\n\n{message['content']}",
+                }
+                return non_system_messages
+
+        return [{"role": "user", "content": f"Instruction: {system_text}"}] + non_system_messages
+
+    def _extract_question_from_content(self, content):
+        text = str(content or "")
+        marker = None
+        for candidate in ("Researcher question:", "Question:"):
+            if candidate in text:
+                marker = candidate
+        if marker is None:
+            return ""
+        question = text.rsplit(marker, 1)[1]
+        question = question.split("\nAnswer:", 1)[0]
+        question = question.split("\n\nResponse rule:", 1)[0]
+        return question.strip()
+
+    def _response_rule(self, question, context=""):
+        question_text = str(question or "")
+        context_text = str(context or "")
+        lowered = question_text.lower()
+        relationship_terms = (
+            "relationship", "association", "associated", "correlation",
+            "linked", "impact", "effect", "finding", "according to the study",
+        )
+        direct_numeric_terms = (
+            "odds ratio", " percentage", "percent", "proportion", "count", "number",
+            "value", "values", "cutoff", "cut-off", "coefficient", "confidence interval",
+            "ci", "p-value", "sample size", "mean", "median", "coded", "code",
+            "classified", "unit",
+        )
+        paper_terms = ("paper", "study", "article", "published", "according to the study", "according to the paper", "systematic review", "meta-analysis")
+        metadata_terms = ("variable", "table", "dataset", "data table", "field", "column", "label", "coded", "code", "non-missing", "records", "entities", "rows")
+        context_has_papers = "## Relevant Published Papers" in context_text or "Source: full-text PDF" in context_text
+        context_has_metadata = "## Relevant Variables" in context_text or "## Relevant Tables" in context_text
+        asks_paper_style = any(term in lowered for term in paper_terms) and not any(term in lowered for term in metadata_terms)
+        source_priority_rule = ""
+        if context_has_papers and context_has_metadata and asks_paper_style:
+            source_priority_rule = (
+                "For published-paper questions, answer from the Relevant Published Papers/full-text PDF text. "
+                "Ignore Relevant Variables and Relevant Tables metadata unless the question explicitly asks for variables, tables, columns, labels, coding, or non-missing records. "
+            )
+        presence_rule = (
+            "Positive evidence has priority over abstention. "
+            "If any sentence, table row, header-expanded row, or labelled excerpt contains the requested value, relationship, or finding, answer it directly; do not abstain. "
+            "Before abstaining, check repeated headers, row labels, timepoints, outcomes, comparison groups, paper excerpts, and adjacent context for an exact match. "
+            "Do not use the abstention answer when a matching answer is present under the requested label, source, study, or outcome. "
+        )
+        abstention_rule = (
+            "Only if the exact requested value, comparison, timepoint, work package, criterion, source, funding body, or finding is not present after that check, "
+            "answer exactly: \"The provided context does not contain the requested information.\" and stop. "
+            "Do not add partial related information after abstaining. "
+            "Do not infer, calculate, or substitute a nearby value from another outcome, age, time period, work package, exposure, table row, cited study, or metadata section. "
+        )
+        anchor_match_rule = (
+            "Wrong-neighbor rule: identify the required anchors in the question, including outcome, measure, subgroup, cohort, model, timepoint, emotion, frequency, exposure, and comparison groups. "
+            "Use only evidence where all requested anchors match. "
+            "Reject nearby rows or sentences if they are about a different outcome, measure, group, model, timepoint, emotion, frequency, exposure, or comparison, even when their numbers look plausible. "
+            "If a matching row contains multiple requested groups, return all requested values from that row; do not omit one group. "
+        )
+        asks_for_relationship = any(term in lowered for term in relationship_terms)
+        asks_for_direct_number = any(term in lowered for term in direct_numeric_terms) or " or " in f" {lowered} "
+        if asks_for_relationship and not asks_for_direct_number:
+            return (
+                "Response rule: " + source_priority_rule + presence_rule + anchor_match_rule +
+                "State the requested relationship or finding in words using only the context. "
+                "Match the requested exposure, outcome, population, cited study, and timepoint exactly. "
+                "If the exact relationship statement is present, restate it rather than abstaining, even if the context also contains adjacent unrelated findings. "
+                "Keep numbers that identify requested groups or timepoints, but do not add model coefficients, "
+                "confidence intervals, p-values, table/figure/page references, variable names, or adjacent numeric details unless the question asks for them. "
+                + abstention_rule
+            )
+        numeric_terms = (
+            "odds ratio", "percentage", "percent", "proportion", "count", "number",
+            "value", "values", "cutoff", "cut-off", "coefficient", "confidence interval",
+            "ci", "p-value", "sample size", "mean", "median", "bmi", "z-score",
+            "quintile", "buffer", "year", "month", "date", "coded", "code",
+            "classified", "unit",
+        )
+        is_numeric_like = any(char.isdigit() for char in question_text) or any(
+            term in lowered for term in numeric_terms
+        )
+        if is_numeric_like:
+            return (
+                "Response rule: " + source_priority_rule + presence_rule + anchor_match_rule +
+                "Extract only the value(s) requested by the question. "
+                "Use nearby headers and labels to choose the matching column, outcome, timepoint, comparison group, or row; do not default to the first number in a row. "
+                "Prefer the value whose row and column labels both match the question. If that matching value is present, return it rather than abstaining. "
+                "Match the requested age, time period, subgroup, exposure, outcome, and direction exactly before copying a number. "
+                "Copy requested numbers, ranges, units, comparison groups, and categories exactly from the context. "
+                "If the question asks for multiple groups or contrasts two reported values, include each requested group. "
+                "Do not include adjacent rows, unrelated categories, p-values, confidence intervals, variable names, "
+                "table/figure/page references, or explanations unless the question asks for them. "
+                + abstention_rule
+            )
+        return (
+            "Response rule: " + source_priority_rule +
+            "Answer directly and concisely using only the context. "
+            "Match the requested paper, study aim, source, criterion, work package, population, exposure, and outcome exactly. "
+            "Do not add table, figure, page, row, variable, or section references unless the question asks for them. "
+            + abstention_rule
+        )
+
+    def _append_response_rule_to_last_user(self, messages):
+        updated = list(messages)
+        for index in range(len(updated) - 1, -1, -1):
+            message = updated[index]
+            if message.get("role") != "user":
+                continue
+            content = message.get("content", "")
+            if "Response rule:" in content:
+                return updated
+            question = self._extract_question_from_content(content)
+            if question:
+                updated[index] = {
+                    "role": "user",
+                    "content": f"{content}\n\n{self._response_rule(question, content)}",
+                }
+            return updated
+        return updated
+
+    def _build_messages(self, inputs, params):
+        use_system_role = bool(params.get("use_system_role", False))
+
+        if isinstance(inputs, list):
+            messages = self._coerce_messages(inputs)
+            if not messages:
+                return [{"role": "user", "content": ""}]
+            if use_system_role:
+                return self._append_response_rule_to_last_user(messages)
+            return self._append_response_rule_to_last_user(self._merge_system_into_first_user(messages))
+
+        if isinstance(inputs, dict) and "messages" in inputs:
+            return self._build_messages(inputs["messages"], params)
+
+        instruction = DEFAULT_INSTRUCTION
+        if isinstance(inputs, dict) and inputs.get("system"):
+            instruction = str(inputs["system"])
+
+        if isinstance(inputs, dict) and "context" in inputs and "question" in inputs:
+            question = str(inputs["question"])
+            context = str(inputs["context"])
+            response_rule = self._response_rule(question, context)
+            if use_system_role:
+                return [
+                    {"role": "system", "content": instruction},
+                    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}\n\n{response_rule}"},
+                ]
+            return [
+                {
+                    "role": "user",
+                    "content": f"Instruction: {instruction}\n\nContext:\n{context}\n\nQuestion: {question}\n\n{response_rule}",
+                }
+            ]
+
+        prompt_text = str(inputs.get("prompt", "")) if isinstance(inputs, dict) else str(inputs)
+        if use_system_role:
+            return self._append_response_rule_to_last_user([
+                {"role": "system", "content": instruction},
+                {"role": "user", "content": prompt_text},
+            ])
+        return self._append_response_rule_to_last_user([
+            {"role": "user", "content": f"Instruction: {instruction}\n\n{prompt_text}"},
+        ])
+
+    def __call__(self, data):
+        inputs = data.get("inputs", "")
+        params = data.get("parameters", {}) or {}
+
+        max_new_tokens = min(int(params.get("max_new_tokens", 64)), 512)
+        max_input_tokens = int(params.get("max_input_tokens", 4096))
+        max_input_tokens = max(512, min(max_input_tokens, 8192))
+        do_sample = bool(params.get("do_sample", False))
+        temperature = float(params.get("temperature", 0.7 if do_sample else 0.0))
+        top_p = float(params.get("top_p", 1.0))
+        repetition_penalty = float(params.get("repetition_penalty", 1.0))
+        no_repeat_ngram_size = int(params.get("no_repeat_ngram_size", 0))
+        debug = bool(params.get("debug", False))
+
+        messages = self._build_messages(inputs, params)
+        prompt = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+
+        enc = self.tokenizer(
+            prompt,
+            return_tensors="pt",
+            add_special_tokens=False,
+            truncation=True,
+            max_length=max_input_tokens,
+        )
+        truncated = enc["input_ids"].shape[-1] >= max_input_tokens
+
+        if torch.cuda.is_available():
+            enc = {key: value.to(self.model.device) for key, value in enc.items()}
+
+        eos_token_id = getattr(self.model.config, "eos_token_id", None)
+        if eos_token_id is None:
+            eos_token_id = self.tokenizer.eos_token_id
+
+        generate_kwargs = dict(
+            **enc,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            repetition_penalty=repetition_penalty,
+            renormalize_logits=True,
+            pad_token_id=self.tokenizer.pad_token_id,
+            eos_token_id=eos_token_id,
+        )
+        if do_sample:
+            generate_kwargs["temperature"] = max(temperature, 1e-5)
+            generate_kwargs["top_p"] = top_p
+        if no_repeat_ngram_size > 0:
+            generate_kwargs["no_repeat_ngram_size"] = no_repeat_ngram_size
+
+        with torch.no_grad():
+            out = self.model.generate(**generate_kwargs)
+
+        generated_ids = out[0][enc["input_ids"].shape[-1]:]
+        text = self.tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
+
+        response = {"generated_text": text}
+        if debug:
+            response["prompt"] = prompt
+            response["messages"] = messages
+            response["input_tokens"] = int(enc["input_ids"].shape[-1])
+            response["truncated"] = bool(truncated)
+        return response
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+      "special": true
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+      "content": "<|reserved_special_token_227|>",
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+      "content": "<|reserved_special_token_228|>",
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+    "128237": {
+      "content": "<|reserved_special_token_229|>",
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+      "single_word": false,
+      "special": true
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+      "content": "<|reserved_special_token_230|>",
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+      "normalized": false,
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+      "special": true
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+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizer"
+}