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Model: bfavro73/qwen2.5-coder-7b-pandas-dpo-aligned Source: Original Platform
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qwen2.5-coder-7b-pandas-dpo-aligned-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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library_name: transformers
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tags: []
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---
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# qwen2.5-coder-7b-pandas-dpo-aligned
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<!-- Provide a quick summary of what the model is/does. -->
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## Introduction
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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
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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
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model was fine-tuned using offline DPO and a preference dataset for Python data analysis.
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Available in GGUF format.
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- Q4_K_M recommended for systems with at least 8GB of RAM. Model file size: 4.68GB
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**Context Window:** up to 128K tokens
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **License:** Apache 2.0
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- **Finetuned from model:** [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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Provides a good foundation for real-world applications such as Coding Agents. It has enhanced coding capabilities and
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maintains strengths in mathematics and general competencies.
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Compute Infrastructure
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The memory you need depends heavily on precision/quantization and context length. *Always consider headroom for runtime and cache!*
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KV/Key-Value cache (scales with context length and batch size).
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Considering standard Memory (bytes)≈params×bytes per parameter rule of thumb:
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**Example 1 (CPU):** Running on CPU (llama.cpp / GGUF):
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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:
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- 16 GB RAM (barely)
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- 32 GB RAM recommended for smoother operation
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**Example 2 (GPU):** For local GPU inference with decent context and speed:
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- Aim for 10–12 GB VRAM (7×10^9×0.5) for a 4‑bit Qwen2.5‑Coder‑7B.
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- Aim for 16 GB VRAM (7×10^9×2) if you want FP16 or very long contexts.
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#### Hardware
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No GPU offload:
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**Absolute Minimum:** 4 vCPUs (4 cores / threads) to run 7B 4‑bit model, but generation will likely feel sluggish, especially with longer responses.
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**Realistic Minimum:** For heavier coding uses with multiple users and long context: 8–12 vCPUs, allows:
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- 8–10 threads for the model.
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- Remaining cores for the app and OS.
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With GPU offload performing all the matrix multiplication 4 vCPUs are sufficient.
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## More Information [optional]
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[More Information Needed]
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151645,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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13
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151645,
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"do_sample": true,
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"eos_token_id": [
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151645
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c834965ef5e72ef0e7e35ba52965f7274b593ffbc5126f80e1e41b0ef851cd4c
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size 15231272152
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3
qwen2.5-coder-7b-pandas-dpo-aligned-q4_k_m.gguf
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qwen2.5-coder-7b-pandas-dpo-aligned-q4_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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size 4683073408
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|im_end|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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