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Model: JdoubleU/careconnect-llama3.2-3b
Source: Original Platform

.gitattributes

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+unsloth.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Modelfile

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+
+FROM /home/app/llama.cpp/careconnect-llama3.2-3b/unsloth.Q8_0.gguf
+TEMPLATE """{{ if .Messages }}
+{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
+{{- if .System }}
+
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+You are a helpful assistant named CareConnect with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question.
+{{- end }}
+{{- end }}<|eot_id|>
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
+{{- if and $.Tools $last }}
+
+Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+
+Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+
+{{ $.Tools }}
+{{- end }}
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+{{- if .ToolCalls }}
+
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+{{- else }}
+
+{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
+{{- end }}
+{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}
+{{- end }}
+{{- else }}
+{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|eom_id|>"
+PARAMETER temperature 1.5
+PARAMETER min_p 0.1

README.md

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+---
+base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- gguf
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded  model
+
+- **Developed by:** JdoubleU
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

adapter_config.json

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+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "q_proj",
+    "gate_proj",
+    "k_proj",
+    "down_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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config.json

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+{
+  "_name_or_path": "unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
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+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.3.18",
+  "use_cache": true,
+  "vocab_size": 128256
+}

handler.py

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+from typing import Any, Dict, List
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            device_map="auto",
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        )
+        self.model.eval()
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        inputs = data.get("inputs", "")
+        parameters = data.get("parameters", {}) or {}
+
+        if isinstance(inputs, list):
+            prompt = "\n".join(str(x) for x in inputs)
+        else:
+            prompt = str(inputs)
+
+        max_new_tokens = int(parameters.get("max_new_tokens", 256))
+        temperature = float(parameters.get("temperature", 0.7))
+        top_p = float(parameters.get("top_p", 0.9))
+        do_sample = bool(parameters.get("do_sample", True))
+
+        messages = parameters.get("messages")
+        if messages and isinstance(messages, list):
+            if hasattr(self.tokenizer, "apply_chat_template"):
+                formatted_prompt = self.tokenizer.apply_chat_template(
+                    messages,
+                    tokenize=False,
+                    add_generation_prompt=True,
+                )
+            else:
+                formatted_prompt = "\n".join(
+                    f"{m.get('role', 'user')}: {m.get('content', '')}" for m in messages
+                )
+        else:
+            formatted_prompt = prompt
+
+        model_inputs = self.tokenizer(
+            formatted_prompt,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+        )
+
+        model_inputs = {k: v.to(self.model.device) for k, v in model_inputs.items()}
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **model_inputs,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=do_sample,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+
+        generated_text = self.tokenizer.decode(
+            outputs[0],
+            skip_special_tokens=True,
+        )
+
+        if generated_text.startswith(formatted_prompt):
+            generated_text = generated_text[len(formatted_prompt):].strip()
+
+        return {
+            "generated_text": generated_text
+        }

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