初始化项目，由ModelHub XC社区提供模型

Model: langtech-innovation/salamandra-7b-instruct-tools-20260108
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+> [!WARNING]
+> **WARNING:** This is a  language model that has undergone instruction tuning for conversational settings that exploit function calling capabilities. It has not been aligned with human preferences. As a result, it may generate outputs that are inappropriate, misleading, biased, or unsafe. These risks can be mitigated through additional post-training stages, which is strongly recommended before deployment in any production system, especially for high-stakes applications.  
+
+### How to use
+```
+from datetime import datetime
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model_id = "BSC-LT/salamandra-7b-instruct"
+
+text = "What is the weather like in Paris today?"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+  )
+
+message = [ { "role": "user", "content": text } ]
+
+tools = [{
+    "type": "function",
+    "name": "get_weather",
+    "description": "Get current temperature for a given location.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "City and country e.g. Bogotá, Colombia"
+            }
+        },
+        "required": [
+            "location"
+        ],
+        "additionalProperties": False
+    }
+}]
+
+
+prompt = tokenizer.apply_chat_template(
+    message,
+    tokenize=False,
+    add_generation_prompt=True,
+    tools=tools
+)
+
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=1000)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+#### Output:
+```text
+<tool_call>
+{"name": "get_weather", "arguments": {"location": "Paris, France"}}
+</tool_call>
+```
+
+
+### Deploy with vllm
+**Deploy the model using vllm docker image.**
+```
+docker run --runtime nvidia --gpus all \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
+    -p 80:80 \
+    vllm/vllm-openai:latest \
+    --model BSC-LT/salamandra-7b-instruct-tools \
+    --enable-auto-tool-choice \
+    --tool-call-parser hermes \
+    --max_model_len 8196 \
+    --port 80
+```
+
+**Then use it with openai api**
+```
+pip install openai
+```
+```
+from openai import OpenAI
+client = OpenAI(
+	    base_url="http://localhost:8080/v1/", 
+	    api_key="hf_xxxx"
+    )
+
+models = client.models.list()
+model = models.data[0].id
+
+system_message = ""
+messages = [{ "role": "system", "content": system_message}] if system_message else []
+messages.append( {"role":"user", "content": "What is the weather like in Paris today?"})
+print(messages)
+chat_completion = client.chat.completions.create(
+    model=model,
+    tools=tools,
+    messages=messages,
+    stream=False,
+    max_tokens=1000,
+    temperature=0.1,
+    frequency_penalty=0.2,
+)
+
+msg = chat_completion.choices[0].message
+
+# --- HANDLE TOOL CALL OR NORMAL CONTENT ---
+
+if not getattr(msg, "tool_calls", None):
+    # Normal assistant message
+    print(msg.content)
+
+    messages.append({
+        "role": "assistant",
+        "content": msg.content
+    })
+
+else:
+    # Assistant tool call message
+    print(msg.tool_calls)
+
+    messages.append({"role": "assistant", "tool_calls": msg.tool_calls})
+
+    # --- Fake tool execution example ---
+    tool_call = msg.tool_calls[0]
+    # Example: handle the get_weather tool
+    if tool_call.function.name == "get_weather":
+        # Fake tool result (this would come from your actual backend)
+        fake_tool_result = '{"temperature": 18, "unit": "C", "description": "Partly cloudy in Paris"}'
+
+        # Append the tool result message so the model can use it in the next turn
+        messages.append({
+            "role": "tool",
+            "tool_call_id": tool_call.id,
+            "name": tool_call.function.name,
+            "content": fake_tool_result,
+        })
+```
+

added_tokens.json

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+{
+  "</tool_call>": 256003,
+  "</tool_response>": 256000,
+  "</tools>": 256002,
+  "<tool_call>": 256004,
+  "<tool_response>": 256001,
+  "<tools>": 256005
+}

config.json

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+{
+  "_name_or_path": "/gpfs/projects/bsc88/hf-models/Salamandra-7b_pre-1.3-160k_sft-2.0_openlicenses_ankush",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 20.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0",
+  "use_cache": true,
+  "vocab_size": 256006
+}

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 5,
+  "max_length": 8192,
+  "pad_token_id": 0,
+  "transformers_version": "4.45.0"
+}

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