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Model: bfavro73/qwen2.5-coder-7b-pandas-dpo-aligned
Source: Original Platform

.gitattributes

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+qwen2.5-coder-7b-pandas-dpo-aligned-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# qwen2.5-coder-7b-pandas-dpo-aligned
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+## Introduction
+
+Fine-tuned version of Qwen's Code-Specific large language model, Qwen2.5-coder-7b. Qwen-2.5 Coder has six mainstream model sizes: 0.5, 1.5, 3, 7, 14 and 32 billion 
+parameter models to meet developer needs. The 7B model version provides a great balance between model representationl capacity and runtime requirements. This 7 Billion parameter
+model was fine-tuned using offline DPO and a preference dataset for Python data analysis.
+
+Available in GGUF format.
+- Q4_K_M recommended for systems with at least 8GB of RAM. Model file size: 4.68GB
+
+**Context Window:** up to 128K tokens
+  
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+
+- **License:** Apache 2.0
+- **Finetuned from model:** [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+
+
+
+
+## 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. -->
+
+Provides a good foundation for real-world applications such as Coding Agents. It has enhanced coding capabilities and
+maintains strengths in mathematics and general competencies.
+
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+
+
+
+### Compute Infrastructure
+
+The memory you need depends heavily on precision/quantization and context length. *Always consider headroom for runtime and cache!*
+KV/Key-Value cache (scales with context length and batch size).
+
+Considering standard Memory (bytes)≈params×bytes per parameter rule of thumb:
+
+**Example 1 (CPU):** Running on CPU (llama.cpp / GGUF):
+
+A 7B model at 4‑bit needs about 3.5 – 4GB (7×10^9x0.5) for weights. With runtime overhead, KV cache, and your app, a practical minimum is:
+
+- 16 GB RAM (barely)
+- 32 GB RAM recommended for smoother operation
+
+**Example 2 (GPU):** For local GPU inference with decent context and speed:
+
+- Aim for 10–12 GB VRAM (7×10^9×0.5) for a 4‑bit Qwen2.5‑Coder‑7B.
+- Aim for 16 GB VRAM (7×10^9×2) if you want FP16 or very long contexts.
+
+
+#### Hardware
+
+No GPU offload:
+
+**Absolute Minimum:** 4 vCPUs (4 cores / threads) to run 7B 4‑bit model, but generation will likely feel sluggish, especially with longer responses.
+
+**Realistic Minimum:** For heavier coding uses with multiple users and long context: 8–12 vCPUs, allows:
+
+- 8–10 threads for the model.
+- Remaining cores for the app and OS.
+
+With GPU offload performing all the matrix multiplication 4 vCPUs are sufficient.
+
+
+## More Information [optional]
+
+[More Information Needed]
+

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151645,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151645,
+  "do_sample": true,
+  "eos_token_id": [
+    151645
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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qwen2.5-coder-7b-pandas-dpo-aligned-q4_k_m.gguf

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_end|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}