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Model: srivarenya/MoM-python-slm-grpo Source: Original Platform
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63
README.md
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---
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license: apache-2.0
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base_model: srivarenya/MoM-python-slm
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pipeline_tag: text-generation
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library_name: transformers
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tags: [code, python, qwen2.5-coder, dora, grpo, rlvr, reinforcement-learning, mixture-of-models, code-generation]
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language: [en]
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---
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# MoM-Python-SLM-GRPO (1.5B)
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The **spec-driven code-generation node** of a **Mixture-of-Models (MoM)** mesh — the GRPO/RLVR-tuned
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successor to [`srivarenya/MoM-python-slm`](https://huggingface.co/srivarenya/MoM-python-slm). Given a
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Python task (optionally with an upstream context packet), it returns reasoning followed by a function.
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It shares the Qwen2.5-Coder tokenizer with the other generative nodes, which is what makes logit-space
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fusion across the mesh valid.
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- **Warm-started from:** [`srivarenya/MoM-python-slm`](https://huggingface.co/srivarenya/MoM-python-slm)
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(DoRA r=64 SFT of Qwen2.5-Coder-1.5B-Instruct)
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- **Method:** GRPO (Group Relative Policy Optimization, RLVR) — a fresh DoRA r=64 adapter trained 500
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steps, then merged.
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- **Reward:** `0.8 · execution + 0.1 · format + 0.1 · LLM-judge`. Execution reward runs each completion
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against the problem's `assert` tests in a sandbox (binary pass/fail) — this is the load-bearing signal.
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Two-sided abstention: `NEED_INPUT` is rewarded only on underspecified prompts.
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- **GRPO config:** β=0 (no KL), asymmetric clip ε=[0.2, 0.25], G=8 completions/prompt, temp=0.9, top_p=0.95,
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lr=1e-5. Problems: 6k execution-verifiable (problem_solving + spec_to_code) + abstention records.
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## Benchmarks (greedy pass@1, same Colab/evalplus harness for all three)
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| Metric | base | SFT (`MoM-python-slm`) | **this model (GRPO)** | GRPO vs SFT |
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|---|---|---|---|---|
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| MBPP | 66.7 | 69.6 | **72.5** | **+2.9** |
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| MBPP+ | — | — | 62.7 | — |
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| domain `problem_solving` (exec) | 0.700 | 0.713 | **0.767** | **+5.4** |
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| domain `spec_to_code` (exec) | 0.632 | 0.714 | **0.729** | +1.5 |
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| domain `api_usage` (application) | — | 0.855 | **0.900** | +4.5 |
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| HumanEval | 68.9 | 70.7 | 67.7 | −3.0 |
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| HumanEval+ | — | — | 62.2 | — |
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| domain `api_signature` (param-recall) | 0.217 | 0.299 | 0.301 | +0.0 |
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## What GRPO did (load-bearing read)
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GRPO is a **specialization trade, not a free lunch.** Gains land on exactly the execution-rewarded,
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spec-driven dimensions — **MBPP +2.9** and **domain problem_solving +5.4** over SFT — while the
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**un-reinforced HumanEval completion format gives back −3.0** (slightly under base). That's the textbook
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RLVR signature: the model sharpens "write a correct function from a spec" (what the MoM node actually
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does) at a small cost to "graft a body under a fixed signature" (a format it never saw a reward for).
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- **Use this model** for the spec-driven node role — it's the strongest on MBPP and the held-out domain eval.
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- **Use the [SFT sibling](https://huggingface.co/srivarenya/MoM-python-slm)** if HumanEval-completion is a
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hard gate — it remains the HumanEval-strongest checkpoint (70.7).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("srivarenya/MoM-python-slm-grpo")
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model = AutoModelForCausalLM.from_pretrained(
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"srivarenya/MoM-python-slm-grpo", dtype="bfloat16", device_map="auto")
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```
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Prompt with the training system prompt + a Python task; the model returns reasoning then code. Reward,
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training recipe, and the self-contained GRPO Colab notebook are in the project repository.
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Next cross-check: **LiveCodeBench** (contamination-resistant), before/after vs the SFT sibling.
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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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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": null,
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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": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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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": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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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": true,
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"transformers_version": "5.12.1",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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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": "5.12.1"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:815cc454a67c91983e0b3f19dc21d444d87a2c47f91b72fd815f39c86448b562
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size 3087467144
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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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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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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": false,
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"local_files_only": false,
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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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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